#loading the neceassary libraries
suppressWarnings({
library(dplyr)
library(readr)
library(caret)
library(tidyverse)
library(rpart)
library(rattle)
library(rpart.plot)
library(RColorBrewer)
library(FSelector)
})
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: ggplot2
## Loading required package: lattice
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: bitops
##
## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.
dataset<-read_csv("depression_anxiety_data.csv")
## Rows: 783 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): gender, who_bmi, depression_severity, anxiety_severity
## dbl (7): id, school_year, age, bmi, phq_score, gad_score, epworth_score
## lgl (8): depressiveness, suicidal, depression_diagnosis, depression_treatmen...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
################################### preproccing steps
##find missing values
is.na(dataset)
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##count missing values
sum(is.na(dataset))
## [1] 41
#delete the missing values
dataset[dataset=="Not Availble"]<- NA
dim(dataset)
## [1] 783 19
dataset = na.omit(dataset)
dim(dataset)
## [1] 757 19
sum(is.na(dataset))
## [1] 0
print(dataset)
## # A tibble: 757 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 747 more rows
## # ℹ 11 more variables: depressiveness <lgl>, suicidal <lgl>,
## # depression_diagnosis <lgl>, depression_treatment <lgl>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
i
#Detect Outliers
#install.packages("outliers")
library(outliers)
Outage = outlier(dataset$age, logical =TRUE)
sum(Outage)
## [1] 2
Find_outlier = which(Outage ==TRUE, arr.ind = TRUE)
Outage
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE
Find_outlier
## [1] 240 590
#
Outbmi= outlier(dataset$bmi, logical =TRUE)
sum(Outbmi)
## [1] 1
Find_outlier2 = which(Outbmi==TRUE, arr.ind = TRUE)
Outbmi
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE
Find_outlier2
## [1] 64
#delet outliers
dataset= dataset[-Find_outlier,]
dataset= dataset[-Find_outlier2,]
# data transformation (change the TRUR and False to 0 and 1 )
dataset$depressiveness = factor (dataset$depressiveness, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <lgl>,
## # depression_diagnosis <lgl>, depression_treatment <lgl>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
dataset$suicidal = factor (dataset$suicidal, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <lgl>, depression_treatment <lgl>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
dataset$depression_diagnosis = factor (dataset$depression_diagnosis, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <lgl>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
dataset$depression_treatment = factor (dataset$depression_treatment, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
dataset$anxiousness = factor (dataset$anxiousness, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <lgl>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
dataset$anxiety_diagnosis = factor (dataset$anxiety_diagnosis, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>
dataset$anxiety_treatment = factor (dataset$anxiety_treatment, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <lgl>
dataset$sleepiness = factor (dataset$sleepiness, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 1 1 19 male 33.3 Class I O… 9 Mild
## 2 2 1 18 male 19.8 Normal 8 Mild
## 3 3 1 19 male 25.1 Overweight 8 Mild
## 4 4 1 18 female 23.7 Normal 19 Moderately severe
## 5 5 1 18 male 25.6 Overweight 6 Mild
## 6 6 1 18 male 22.1 Normal 3 None-minimal
## 7 7 1 18 male 22.4 Normal 6 Mild
## 8 8 1 19 male 20.5 Normal 4 None-minimal
## 9 9 1 20 male 21.2 Normal 11 Moderate
## 10 10 1 19 male 24.5 Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>
#Discretization the values based on fixed set of predetermined criteria
#1- bmi discretizatio (indicate high body fatness)
breaks <- c(18,18.4,24.9,29.9,34.9,39.9,100)
dataset$bmi= cut(dataset$bmi, breaks = breaks)
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <fct> <chr> <dbl> <chr>
## 1 1 1 19 male (29.9,3… Class … 9 Mild
## 2 2 1 18 male (18.4,2… Normal 8 Mild
## 3 3 1 19 male (24.9,2… Overwe… 8 Mild
## 4 4 1 18 female (18.4,2… Normal 19 Moderately severe
## 5 5 1 18 male (24.9,2… Overwe… 6 Mild
## 6 6 1 18 male (18.4,2… Normal 3 None-minimal
## 7 7 1 18 male (18.4,2… Normal 6 Mild
## 8 8 1 19 male (18.4,2… Normal 4 None-minimal
## 9 9 1 20 male (18.4,2… Normal 11 Moderate
## 10 10 1 19 male (18.4,2… Normal 6 Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>
#2- phq test score discretizatio(Depression Test Questionnaire)
breaks <- c(0,4,9,14,19,27)
dataset$phq_score= cut(dataset$phq_score, breaks = breaks)
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <fct> <chr> <fct> <chr>
## 1 1 1 19 male (29.9,3… Class … (4,9] Mild
## 2 2 1 18 male (18.4,2… Normal (4,9] Mild
## 3 3 1 19 male (24.9,2… Overwe… (4,9] Mild
## 4 4 1 18 female (18.4,2… Normal (14,19] Moderately severe
## 5 5 1 18 male (24.9,2… Overwe… (4,9] Mild
## 6 6 1 18 male (18.4,2… Normal (0,4] None-minimal
## 7 7 1 18 male (18.4,2… Normal (4,9] Mild
## 8 8 1 19 male (18.4,2… Normal (0,4] None-minimal
## 9 9 1 20 male (18.4,2… Normal (9,14] Moderate
## 10 10 1 19 male (18.4,2… Normal (4,9] Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>
#3- gad test score discretization(Generalised Anxiety Disorder Assessment)
breaks <- c(0,4,9,14,21)
dataset$gad_score= cut(dataset$gad_score, breaks = breaks)
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <fct> <chr> <fct> <chr>
## 1 1 1 19 male (29.9,3… Class … (4,9] Mild
## 2 2 1 18 male (18.4,2… Normal (4,9] Mild
## 3 3 1 19 male (24.9,2… Overwe… (4,9] Mild
## 4 4 1 18 female (18.4,2… Normal (14,19] Moderately severe
## 5 5 1 18 male (24.9,2… Overwe… (4,9] Mild
## 6 6 1 18 male (18.4,2… Normal (0,4] None-minimal
## 7 7 1 18 male (18.4,2… Normal (4,9] Mild
## 8 8 1 19 male (18.4,2… Normal (0,4] None-minimal
## 9 9 1 20 male (18.4,2… Normal (9,14] Moderate
## 10 10 1 19 male (18.4,2… Normal (4,9] Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <fct>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>
#4- Epworth test score discretizatio (Sleepiness Scale)
breaks <- c(0,10,14,17,24)
dataset$epworth_score= cut(dataset$epworth_score, breaks = breaks)
print(dataset)
## # A tibble: 754 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <fct> <chr> <fct> <chr>
## 1 1 1 19 male (29.9,3… Class … (4,9] Mild
## 2 2 1 18 male (18.4,2… Normal (4,9] Mild
## 3 3 1 19 male (24.9,2… Overwe… (4,9] Mild
## 4 4 1 18 female (18.4,2… Normal (14,19] Moderately severe
## 5 5 1 18 male (24.9,2… Overwe… (4,9] Mild
## 6 6 1 18 male (18.4,2… Normal (0,4] None-minimal
## 7 7 1 18 male (18.4,2… Normal (4,9] Mild
## 8 8 1 19 male (18.4,2… Normal (0,4] None-minimal
## 9 9 1 20 male (18.4,2… Normal (9,14] Moderate
## 10 10 1 19 male (18.4,2… Normal (4,9] Mild
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <fct>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <fct>, sleepiness <fct>
######### end of preproccing
########### diagram for the class label (depressivness)
boolean_data <- dataset$depressiveness
print(boolean_data)
## [1] 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
## [38] 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0
## [75] 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0
## [112] 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0
## [149] 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1
## [186] 0 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1
## [223] 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0
## [260] 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 0 1 1 0 1 1 1 0 0 1
## [297] 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0
## [334] 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1
## [371] 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1
## [408] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0
## [445] 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [482] 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0
## [519] 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0
## [556] 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## [593] 0 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0
## [630] 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0
## [667] 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 0
## [704] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1
## [741] 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## Levels: 1 0
# Count the occurrences of TRUE and FALSE values
true_count <- sum(boolean_data==1)
print(true_count)
## [1] 203
false_count <- sum(boolean_data==0)
print(false_count)
## [1] 551
# Values for the bar chart
bar_heights <- c(true_count, false_count)
# Names for the bars
bar_names <- c("TRUE", "FALSE")
# Create a bar chart
barplot(bar_heights, names.arg = bar_names, col = c("blue", "red"),
xlab = "Boolean Values", ylab = "Count",
main = "class label (depressiveness)")
# explore the dataset
str(dataset)
## tibble [754 × 19] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:754] 1 2 3 4 5 6 7 8 9 10 ...
## $ school_year : num [1:754] 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num [1:754] 19 18 19 18 18 18 18 19 20 19 ...
## $ gender : chr [1:754] "male" "male" "male" "female" ...
## $ bmi : Factor w/ 6 levels "(18,18.4]","(18.4,24.9]",..: 4 2 3 2 3 2 2 2 2 2 ...
## $ who_bmi : chr [1:754] "Class I Obesity" "Normal" "Overweight" "Normal" ...
## $ phq_score : Factor w/ 5 levels "(0,4]","(4,9]",..: 2 2 2 4 2 1 2 1 3 2 ...
## $ depression_severity : chr [1:754] "Mild" "Mild" "Mild" "Moderately severe" ...
## $ depressiveness : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
## $ suicidal : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 2 2 ...
## $ depression_diagnosis: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ depression_treatment: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ gad_score : Factor w/ 4 levels "(0,4]","(4,9]",..: 3 2 2 4 3 1 1 2 2 1 ...
## $ anxiety_severity : chr [1:754] "Moderate" "Mild" "Mild" "Severe" ...
## $ anxiousness : Factor w/ 2 levels "1","0": 1 2 2 1 1 2 2 2 2 2 ...
## $ anxiety_diagnosis : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ anxiety_treatment : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ epworth_score : Factor w/ 4 levels "(0,10]","(10,14]",..: 1 2 1 2 1 1 1 1 1 1 ...
## $ sleepiness : Factor w/ 2 levels "1","0": 2 1 2 1 2 2 2 2 2 2 ...
## - attr(*, "na.action")= 'omit' Named int [1:26] 12 23 24 25 30 40 51 128 158 167 ...
## ..- attr(*, "names")= chr [1:26] "12" "23" "24" "25" ...
glimpse(dataset)
## Rows: 754
## Columns: 19
## $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16…
## $ school_year <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ age <dbl> 19, 18, 19, 18, 18, 18, 18, 19, 20, 19, 18, 19, 1…
## $ gender <chr> "male", "male", "male", "female", "male", "male",…
## $ bmi <fct> "(29.9,34.9]", "(18.4,24.9]", "(24.9,29.9]", "(18…
## $ who_bmi <chr> "Class I Obesity", "Normal", "Overweight", "Norma…
## $ phq_score <fct> "(4,9]", "(4,9]", "(4,9]", "(14,19]", "(4,9]", "(…
## $ depression_severity <chr> "Mild", "Mild", "Mild", "Moderately severe", "Mil…
## $ depressiveness <fct> 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0…
## $ suicidal <fct> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ depression_diagnosis <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ depression_treatment <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ gad_score <fct> "(9,14]", "(4,9]", "(4,9]", "(14,21]", "(9,14]", …
## $ anxiety_severity <chr> "Moderate", "Mild", "Mild", "Severe", "Moderate",…
## $ anxiousness <fct> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0…
## $ anxiety_diagnosis <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ anxiety_treatment <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ epworth_score <fct> "(0,10]", "(10,14]", "(0,10]", "(10,14]", "(0,10]…
## $ sleepiness <fct> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0…
# setting seed to generate a
# reproducible random sampling
set.seed(123)
# there is still missing value
dataset <- na.omit(dataset)
# make sure that the outcome column is a factor or numeric.
# we need to make sure the class lable is factor
str(dataset$depressiveness)
## Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
# we can see the datatype is binary as true and false
#convert it to factor
dataset$depressiveness <- as.factor(dataset$depressiveness)
#make data frame to track the process of the different tree
# Create an empty dataframe
evaluation_df <- data.frame(tree = character(), accuracy = numeric(), Atrribute=character(),cv_K=character(),stringsAsFactors = FALSE)
folds <- createFolds(dataset$depressiveness, k = 5) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
#build the model
tree1 <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = "information"))
# train result
trained_model <- tree1$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.28655835 0.71344165)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.00000000 0.00000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.11700183 0.88299817)
## 6) suicidal0< 0.5 42 0 1 (1.00000000 0.00000000) *
## 7) suicidal0>=0.5 505 22 0 (0.04356436 0.95643564) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 208.133991 208.133991
## suicidal0 depression_severitySevere
## 106.965905 15.280844
## phq_score(19,27] epworth_score(17,24]
## 15.280844 10.187229
## bmi(39.9,100] who_bmiClass III Obesity
## 3.202061 3.202061
## bmi(34.9,39.9] who_bmiClass II Obesity
## 2.546807 2.546807
#summary of the trainig
summary(trained_model)
## Call:
## (function (formula, data, weights, subset, na.action = na.rpart,
## method, model = FALSE, x = FALSE, y = TRUE, parms, control,
## cost, ...)
## {
## Call <- match.call()
## if (is.data.frame(model)) {
## m <- model
## model <- FALSE
## }
## else {
## indx <- match(c("formula", "data", "weights", "subset"),
## names(Call), nomatch = 0)
## if (indx[1] == 0)
## stop("a 'formula' argument is required")
## temp <- Call[c(1, indx)]
## temp$na.action <- na.action
## temp[[1]] <- quote(stats::model.frame)
## m <- eval.parent(temp)
## }
## Terms <- attr(m, "terms")
## if (any(attr(Terms, "order") > 1))
## stop("Trees cannot handle interaction terms")
## Y <- model.response(m)
## wt <- model.weights(m)
## if (any(wt < 0))
## stop("negative weights not allowed")
## if (!length(wt))
## wt <- rep(1, nrow(m))
## offset <- model.offset(m)
## X <- rpart.matrix(m)
## nobs <- nrow(X)
## nvar <- ncol(X)
## if (missing(method)) {
## method <- if (is.factor(Y) || is.character(Y))
## "class"
## else if (inherits(Y, "Surv"))
## "exp"
## else if (is.matrix(Y))
## "poisson"
## else "anova"
## }
## if (is.list(method)) {
## mlist <- method
## method <- "user"
## init <- if (missing(parms))
## mlist$init(Y, offset, wt = wt)
## else mlist$init(Y, offset, parms, wt)
## keep <- rpartcallback(mlist, nobs, init)
## method.int <- 4
## parms <- init$parms
## }
## else {
## method.int <- pmatch(method, c("anova", "poisson", "class",
## "exp"))
## if (is.na(method.int))
## stop("Invalid method")
## method <- c("anova", "poisson", "class", "exp")[method.int]
## if (method.int == 4)
## method.int <- 2
## init <- if (missing(parms))
## get(paste("rpart", method, sep = "."), envir = environment())(Y,
## offset, , wt)
## else get(paste("rpart", method, sep = "."), envir = environment())(Y,
## offset, parms, wt)
## ns <- asNamespace("rpart")
## if (!is.null(init$print))
## environment(init$print) <- ns
## if (!is.null(init$summary))
## environment(init$summary) <- ns
## if (!is.null(init$text))
## environment(init$text) <- ns
## }
## Y <- init$y
## xlevels <- .getXlevels(Terms, m)
## cats <- rep(0, ncol(X))
## if (!is.null(xlevels)) {
## indx <- match(names(xlevels), colnames(X), nomatch = 0)
## cats[indx] <- (unlist(lapply(xlevels, length)))[indx >
## 0]
## }
## extraArgs <- list(...)
## if (length(extraArgs)) {
## controlargs <- names(formals(rpart.control))
## indx <- match(names(extraArgs), controlargs, nomatch = 0)
## if (any(indx == 0))
## stop(gettextf("Argument %s not matched", names(extraArgs)[indx ==
## 0]), domain = NA)
## }
## controls <- rpart.control(...)
## if (!missing(control))
## controls[names(control)] <- control
## xval <- controls$xval
## if (is.null(xval) || (length(xval) == 1 && xval == 0) ||
## method == "user") {
## xgroups <- 0
## xval <- 0
## }
## else if (length(xval) == 1) {
## xgroups <- sample(rep(1:xval, length.out = nobs), nobs,
## replace = FALSE)
## }
## else if (length(xval) == nobs) {
## xgroups <- xval
## xval <- length(unique(xgroups))
## }
## else {
## if (!is.null(attr(m, "na.action"))) {
## temp <- as.integer(attr(m, "na.action"))
## xval <- xval[-temp]
## if (length(xval) == nobs) {
## xgroups <- xval
## xval <- length(unique(xgroups))
## }
## else stop("Wrong length for 'xval'")
## }
## else stop("Wrong length for 'xval'")
## }
## if (missing(cost))
## cost <- rep(1, nvar)
## else {
## if (length(cost) != nvar)
## stop("Cost vector is the wrong length")
## if (any(cost <= 0))
## stop("Cost vector must be positive")
## }
## tfun <- function(x) if (is.matrix(x))
## rep(is.ordered(x), ncol(x))
## else is.ordered(x)
## labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
## isord <- unlist(lapply(m[labs], tfun))
## storage.mode(X) <- "double"
## storage.mode(wt) <- "double"
## temp <- as.double(unlist(init$parms))
## if (!length(temp))
## temp <- 0
## rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord),
## method = as.integer(method.int), as.double(unlist(controls)),
## temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)),
## X, wt, as.integer(init$numy), as.double(cost))
## nsplit <- nrow(rpfit$isplit)
## ncat <- if (!is.null(rpfit$csplit))
## nrow(rpfit$csplit)
## else 0
## if (nsplit == 0)
## xval <- 0
## numcp <- ncol(rpfit$cptable)
## temp <- if (nrow(rpfit$cptable) == 3)
## c("CP", "nsplit", "rel error")
## else c("CP", "nsplit", "rel error", "xerror", "xstd")
## dimnames(rpfit$cptable) <- list(temp, 1:numcp)
## tname <- c("<leaf>", colnames(X))
## splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5,
## dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count",
## "ncat", "improve", "index", "adj")))
## index <- rpfit$inode[, 2]
## nadd <- sum(isord[rpfit$isplit[, 1]])
## if (nadd > 0) {
## newc <- matrix(0, nadd, max(cats))
## cvar <- rpfit$isplit[, 1]
## indx <- isord[cvar]
## cdir <- splits[indx, 2]
## ccut <- floor(splits[indx, 4])
## splits[indx, 2] <- cats[cvar[indx]]
## splits[indx, 4] <- ncat + 1:nadd
## for (i in 1:nadd) {
## newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
## newc[i, 1:ccut[i]] <- as.integer(cdir[i])
## }
## catmat <- if (ncat == 0)
## newc
## else {
## cs <- rpfit$csplit
## ncs <- ncol(cs)
## ncc <- ncol(newc)
## if (ncs < ncc)
## cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
## rbind(cs, newc)
## }
## ncat <- ncat + nadd
## }
## else catmat <- rpfit$csplit
## if (nsplit == 0) {
## frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[,
## 5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1],
## yval = rpfit$dnode[, 4], complexity = rpfit$dnode[,
## 2], ncompete = 0, nsurrogate = 0)
## }
## else {
## temp <- ifelse(index == 0, 1, index)
## svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
## frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar +
## 1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3],
## dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4],
## complexity = rpfit$dnode[, 2], ncompete = pmax(0,
## rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[,
## 4])
## }
## if (method.int == 3) {
## numclass <- init$numresp - 2
## nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
## temp <- pmax(1, init$counts)
## temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
## yprob <- temp/rowSums(temp)
## yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass +
## 1)
## frame$yval2 <- cbind(yval2, yprob, nodeprob)
## }
## else if (init$numresp > 1)
## frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
## if (is.null(init$summary))
## stop("Initialization routine is missing the 'summary' function")
## functions <- if (is.null(init$print))
## list(summary = init$summary)
## else list(summary = init$summary, print = init$print)
## if (!is.null(init$text))
## functions <- c(functions, list(text = init$text))
## if (method == "user")
## functions <- c(functions, mlist)
## where <- rpfit$which
## names(where) <- row.names(m)
## ans <- list(frame = frame, where = where, call = Call, terms = Terms,
## cptable = t(rpfit$cptable), method = method, parms = init$parms,
## control = controls, functions = functions, numresp = init$numresp)
## if (nsplit)
## ans$splits = splits
## if (ncat > 0)
## ans$csplit <- catmat + 2
## if (nsplit)
## ans$variable.importance <- importance(ans)
## if (model) {
## ans$model <- m
## if (missing(y))
## y <- FALSE
## }
## if (y)
## ans$y <- Y
## if (x) {
## ans$x <- X
## ans$wt <- wt
## }
## ans$ordered <- isord
## if (!is.null(attr(m, "na.action")))
## ans$na.action <- attr(m, "na.action")
## if (!is.null(xlevels))
## attr(ans, "xlevels") <- xlevels
## if (method == "class")
## attr(ans, "ylevels") <- init$ylevels
## class(ans) <- "rpart"
## ans
## })(formula = .outcome ~ ., data = list(c(1, 2, 3, 4, 5, 6, 7,
## 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 26, 27, 28,
## 29, 31, 33, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49,
## 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68,
## 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
## 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
## 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,
## 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 129,
## 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
## 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 157,
## 159, 160, 162, 163, 164, 165, 166, 168, 170, 171, 172, 173, 175,
## 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190,
## 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
## 204, 206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218,
## 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
## 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
## 247, 248, 250, 251, 253, 254, 255, 256, 257, 258, 259, 260, 261,
## 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274,
## 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288,
## 289, 290, 292, 293, 294, 295, 297, 300, 301, 302, 303, 304, 305,
## 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318,
## 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,
## 333, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347,
## 348, 349, 350, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362,
## 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375,
## 376, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 390, 391,
## 392, 394, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 409,
## 410, 411, 412, 413, 414, 416, 417, 418, 419, 420, 421, 422, 423,
## 424, 425, 426, 428, 429, 431, 432, 433, 434, 435, 437, 438, 440,
## 441, 442, 443, 444, 446, 448, 449, 450, 451, 453, 454, 455, 456,
## 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,
## 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483,
## 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496,
## 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 510,
## 511, 512, 513, 514, 515, 516, 517, 520, 521, 522, 524, 525, 527,
## 528, 529, 532, 533, 534, 535, 536, 538, 539, 540, 543, 544, 545,
## 546, 547, 548, 549, 550, 551, 553, 554, 555, 557, 558, 559, 560,
## 561, 562, 563, 565, 567, 568, 569, 570, 572, 573, 574, 575, 576,
## 577, 578, 581, 582, 583, 584, 585, 586, 587, 588, 590, 591, 592,
## 594, 595, 596, 597, 599, 600, 602, 603, 604, 605, 606, 608, 609,
## 610, 611, 612, 613, 615, 616, 617, 618, 619, 620, 621, 622, 623,
## 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 637, 639,
## 640, 641, 642, 643, 644, 645, 646, 647, 650, 651, 653, 654, 655,
## 656, 657, 658, 659, 660, 661, 662, 663, 664, 667, 668, 669, 670,
## 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684,
## 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698,
## 700, 701, 705, 706, 707, 708, 709, 711, 712, 714, 715, 716, 717,
## 718, 719, 720, 721, 722, 723, 724, 725, 726, 728, 729, 730, 731,
## 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 745,
## 746, 747, 749, 750, 751, 753, 755, 756, 757, 758, 759, 760, 761,
## 762, 764, 765, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776,
## 777, 778, 779, 780, 781, 782), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), c(19, 18, 19,
## 18, 18, 18, 18, 19, 20, 19, 18, 19, 18, 18, 18, 18, 18, 18, 18,
## 18, 18, 19, 18, 19, 20, 18, 19, 18, 18, 19, 19, 18, 19, 19, 18,
## 18, 19, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 19, 19, 19, 19,
## 18, 18, 18, 20, 24, 19, 19, 20, 18, 20, 19, 19, 20, 20, 19, 18,
## 18, 20, 20, 18, 19, 18, 18, 18, 19, 18, 19, 18, 20, 19, 18, 18,
## 19, 18, 19, 18, 18, 18, 18, 19, 19, 18, 19, 19, 18, 18, 18, 18,
## 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 18, 18,
## 18, 20, 21, 18, 19, 19, 18, 19, 19, 19, 18, 18, 23, 19, 18, 19,
## 18, 20, 18, 19, 19, 18, 18, 20, 18, 18, 18, 20, 19, 19, 18, 19,
## 18, 19, 18, 20, 18, 18, 18, 18, 20, 18, 18, 19, 18, 19, 23, 21,
## 20, 20, 26, 20, 19, 20, 19, 20, 19, 19, 19, 23, 19, 19, 19, 20,
## 18, 19, 21, 20, 19, 19, 20, 20, 24, 20, 20, 20, 21, 20, 19, 19,
## 18, 24, 19, 19, 19, 19, 21, 19, 19, 18, 19, 23, 21, 22, 20, 20,
## 20, 19, 19, 19, 19, 21, 19, 19, 19, 20, 20, 18, 21, 19, 20, 19,
## 20, 19, 19, 19, 19, 19, 21, 20, 19, 20, 24, 22, 19, 20, 21, 19,
## 20, 21, 19, 21, 20, 25, 19, 20, 20, 19, 20, 23, 20, 20, 19, 20,
## 20, 20, 19, 23, 20, 19, 19, 20, 19, 19, 19, 19, 19, 19, 18, 19,
## 19, 19, 19, 19, 20, 19, 20, 24, 19, 19, 19, 20, 21, 22, 19, 19,
## 23, 19, 19, 18, 20, 19, 19, 19, 20, 19, 20, 20, 19, 21, 19, 19,
## 19, 22, 22, 19, 22, 20, 19, 19, 21, 22, 19, 20, 18, 20, 19, 20,
## 20, 19, 19, 19, 21, 19, 18, 20, 21, 19, 19, 20, 19, 19, 19, 20,
## 18, 22, 19, 19, 20, 19, 19, 19, 20, 19, 20, 19, 20, 20, 19, 20,
## 20, 19, 21, 19, 23, 19, 19, 20, 20, 19, 24, 19, 21, 20, 19, 19,
## 19, 19, 21, 19, 19, 20, 20, 19, 20, 19, 19, 19, 19, 19, 19, 23,
## 19, 19, 19, 20, 20, 19, 19, 20, 19, 20, 20, 19, 25, 20, 21, 19,
## 21, 20, 19, 20, 19, 20, 20, 20, 21, 20, 20, 20, 22, 20, 20, 20,
## 21, 22, 20, 20, 21, 20, 20, 21, 22, 26, 20, 20, 20, 20, 20, 20,
## 21, 21, 20, 20, 20, 20, 20, 21, 20, 21, 20, 28, 20, 21, 20, 21,
## 20, 21, 22, 23, 21, 23, 19, 22, 21, 21, 20, 19, 20, 22, 21, 20,
## 20, 20, 21, 20, 21, 20, 21, 21, 21, 22, 20, 22, 20, 20, 20, 21,
## 23, 23, 22, 21, 21, 21, 21, 21, 21, 21, 22, 21, 22, 23, 22, 21,
## 22, 22, 21, 22, 18, 21, 22, 22, 25, 21, 22, 21, 21, 21, 21, 21,
## 23, 21, 21, 20, 27, 21, 22, 22, 22, 23, 21, 23, 22, 22, 21, 24,
## 21, 22, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 21, 21, 21,
## 21, 22, 22, 22, 21, 22, 24, 22, 21, 22, 21, 24, 21, 21, 21, 22,
## 21, 22, 21, 21, 22, 22, 25, 21, 24, 22, 22, 21, 21, 21, 21, 22,
## 22, 22, 22, 26, 21, 21, 22, 21, 21, 21, 22, 21, 22, 22, 21, 22,
## 22, 21, 22, 20, 21, 21, 21, 21, 20, 23, 21, 22, 21, 21, 21, 21,
## 21, 21, 21, 21, 21, 21, 22, 21, 21, 22, 21, 21, 21, 21, 23, 22,
## 21, 21, 24, 22, 22, 22, 22, 22, 22, 26, 22, 22, 21, 21, 22, 22,
## 30, 23, 22, 22, 22, 23, 24, 23, 22, 23, 22, 22, 24, 22, 22, 22,
## 23, 24, 24, 23, 22, 22, 21, 22, 23, 22, 23, 22, 23, 22, 24, 22,
## 22, 22), c(1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
## 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,
## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,
## 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,
## 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0,
## 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,
## 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,
## 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,
## 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,
## 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,
## 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,
## 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1,
## 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
## 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1,
## 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
## 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0,
## 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
## 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
## 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,
## 0, 1, 0, 0, 1, 1, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,
## 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
## 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
## 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
## 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(0, 1, 1, 0, 0, 0, 0, 1, 1, 0,
## 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1,
## 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,
## 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
## 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,
## 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
## 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1,
## 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
## 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
## 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
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## 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
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## 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,
## 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,
## 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,
## 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
## 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0,
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
## 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,
## 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1), c(0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
## 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
## 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), c(0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(1,
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
## 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,
## 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0,
## 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
## 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,
## 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,
## 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0), c(2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2,
## 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1,
## 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2,
## 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2,
## 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2,
## 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2,
## 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2,
## 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1,
## 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2,
## 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1,
## 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,
## 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,
## 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1,
## 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1,
## 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2,
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2,
## 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2,
## 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1,
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1,
## 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2,
## 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2,
## 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2,
## 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2,
## 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2,
## 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2,
## 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1,
## 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2,
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 1)), parms = list("information"),
## control = list(20, 7, 0, 4, 5, 2, 0, 30, 0))
## n= 677
##
## CP nsplit rel error
## 1 0.6701031 0 1.0000000
## 2 0.2164948 1 0.3298969
## 3 0.1030928 2 0.1134021
##
## Variable importance
## depression_severityModerate phq_score(9,14]
## 36 36
## suicidal0 depression_severitySevere
## 19 3
## phq_score(19,27] epworth_score(17,24]
## 3 2
## bmi(39.9,100] who_bmiClass III Obesity
## 1 1
##
## Node number 1: 677 observations, complexity param=0.6701031
## predicted class=0 expected loss=0.2865583 P(node) =1
## class counts: 194 483
## probabilities: 0.287 0.713
## left son=2 (130 obs) right son=3 (547 obs)
## Primary splits:
## phq_score(9,14] < 0.5 to the right, improve=208.13400, (0 missing)
## depression_severityModerate < 0.5 to the right, improve=208.13400, (0 missing)
## phq_score(4,9] < 0.5 to the left, improve= 95.97735, (0 missing)
## suicidal0 < 0.5 to the left, improve= 85.73113, (0 missing)
## depression_severityNone-minimal < 0.5 to the left, improve= 71.79485, (0 missing)
## Surrogate splits:
## depression_severityModerate < 0.5 to the right, agree=1.000, adj=1.000, (0 split)
## bmi(39.9,100] < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
## who_bmiClass III Obesity < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
##
## Node number 2: 130 observations
## predicted class=1 expected loss=0 P(node) =0.1920236
## class counts: 130 0
## probabilities: 1.000 0.000
##
## Node number 3: 547 observations, complexity param=0.2164948
## predicted class=0 expected loss=0.1170018 P(node) =0.8079764
## class counts: 64 483
## probabilities: 0.117 0.883
## left son=6 (42 obs) right son=7 (505 obs)
## Primary splits:
## suicidal0 < 0.5 to the left, improve=106.96590, (0 missing)
## phq_score(14,19] < 0.5 to the right, improve=100.78320, (0 missing)
## depression_severityModerately severe < 0.5 to the right, improve=100.78320, (0 missing)
## anxiousness0 < 0.5 to the left, improve= 49.96506, (0 missing)
## gad_score(14,21] < 0.5 to the right, improve= 44.23279, (0 missing)
## Surrogate splits:
## phq_score(19,27] < 0.5 to the right, agree=0.934, adj=0.143, (0 split)
## depression_severitySevere < 0.5 to the right, agree=0.934, adj=0.143, (0 split)
## epworth_score(17,24] < 0.5 to the right, agree=0.931, adj=0.095, (0 split)
## bmi(34.9,39.9] < 0.5 to the right, agree=0.925, adj=0.024, (0 split)
## who_bmiClass II Obesity < 0.5 to the right, agree=0.925, adj=0.024, (0 split)
##
## Node number 6: 42 observations
## predicted class=1 expected loss=0 P(node) =0.0620384
## class counts: 42 0
## probabilities: 1.000 0.000
##
## Node number 7: 505 observations
## predicted class=0 expected loss=0.04356436 P(node) =0.745938
## class counts: 22 483
## probabilities: 0.044 0.956
#accuracy of the tree
accuracy <- tree1$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.956061959880227" "Accuracy: 0.939064599518454"
## [3] "Accuracy: 0.787680324123019"
# get the average accuracy across all fold
accuracy <- mean(tree1$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.8942689611739"
tree1 <- valuation_df <- data.frame(tree = "tree1", accuracy = accuracy, Atrribute="information gain",
cv_K=5)
evaluation_df <- rbind(evaluation_df, tree1)
# visulize the tree'
fancyRpartPlot(trained_model, caption = NULL)
folds <- createFolds(dataset$depressiveness, k = 5) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart")
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.2865583 0.7134417)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.0000000 0.0000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.1170018 0.8829982) *
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 163.791596 163.791596
## bmi(39.9,100] who_bmiClass III Obesity
## 2.519871 2.519871
#summary of the trainig
summary(trained_model)
## Call:
## (function (formula, data, weights, subset, na.action = na.rpart,
## method, model = FALSE, x = FALSE, y = TRUE, parms, control,
## cost, ...)
## {
## Call <- match.call()
## if (is.data.frame(model)) {
## m <- model
## model <- FALSE
## }
## else {
## indx <- match(c("formula", "data", "weights", "subset"),
## names(Call), nomatch = 0)
## if (indx[1] == 0)
## stop("a 'formula' argument is required")
## temp <- Call[c(1, indx)]
## temp$na.action <- na.action
## temp[[1]] <- quote(stats::model.frame)
## m <- eval.parent(temp)
## }
## Terms <- attr(m, "terms")
## if (any(attr(Terms, "order") > 1))
## stop("Trees cannot handle interaction terms")
## Y <- model.response(m)
## wt <- model.weights(m)
## if (any(wt < 0))
## stop("negative weights not allowed")
## if (!length(wt))
## wt <- rep(1, nrow(m))
## offset <- model.offset(m)
## X <- rpart.matrix(m)
## nobs <- nrow(X)
## nvar <- ncol(X)
## if (missing(method)) {
## method <- if (is.factor(Y) || is.character(Y))
## "class"
## else if (inherits(Y, "Surv"))
## "exp"
## else if (is.matrix(Y))
## "poisson"
## else "anova"
## }
## if (is.list(method)) {
## mlist <- method
## method <- "user"
## init <- if (missing(parms))
## mlist$init(Y, offset, wt = wt)
## else mlist$init(Y, offset, parms, wt)
## keep <- rpartcallback(mlist, nobs, init)
## method.int <- 4
## parms <- init$parms
## }
## else {
## method.int <- pmatch(method, c("anova", "poisson", "class",
## "exp"))
## if (is.na(method.int))
## stop("Invalid method")
## method <- c("anova", "poisson", "class", "exp")[method.int]
## if (method.int == 4)
## method.int <- 2
## init <- if (missing(parms))
## get(paste("rpart", method, sep = "."), envir = environment())(Y,
## offset, , wt)
## else get(paste("rpart", method, sep = "."), envir = environment())(Y,
## offset, parms, wt)
## ns <- asNamespace("rpart")
## if (!is.null(init$print))
## environment(init$print) <- ns
## if (!is.null(init$summary))
## environment(init$summary) <- ns
## if (!is.null(init$text))
## environment(init$text) <- ns
## }
## Y <- init$y
## xlevels <- .getXlevels(Terms, m)
## cats <- rep(0, ncol(X))
## if (!is.null(xlevels)) {
## indx <- match(names(xlevels), colnames(X), nomatch = 0)
## cats[indx] <- (unlist(lapply(xlevels, length)))[indx >
## 0]
## }
## extraArgs <- list(...)
## if (length(extraArgs)) {
## controlargs <- names(formals(rpart.control))
## indx <- match(names(extraArgs), controlargs, nomatch = 0)
## if (any(indx == 0))
## stop(gettextf("Argument %s not matched", names(extraArgs)[indx ==
## 0]), domain = NA)
## }
## controls <- rpart.control(...)
## if (!missing(control))
## controls[names(control)] <- control
## xval <- controls$xval
## if (is.null(xval) || (length(xval) == 1 && xval == 0) ||
## method == "user") {
## xgroups <- 0
## xval <- 0
## }
## else if (length(xval) == 1) {
## xgroups <- sample(rep(1:xval, length.out = nobs), nobs,
## replace = FALSE)
## }
## else if (length(xval) == nobs) {
## xgroups <- xval
## xval <- length(unique(xgroups))
## }
## else {
## if (!is.null(attr(m, "na.action"))) {
## temp <- as.integer(attr(m, "na.action"))
## xval <- xval[-temp]
## if (length(xval) == nobs) {
## xgroups <- xval
## xval <- length(unique(xgroups))
## }
## else stop("Wrong length for 'xval'")
## }
## else stop("Wrong length for 'xval'")
## }
## if (missing(cost))
## cost <- rep(1, nvar)
## else {
## if (length(cost) != nvar)
## stop("Cost vector is the wrong length")
## if (any(cost <= 0))
## stop("Cost vector must be positive")
## }
## tfun <- function(x) if (is.matrix(x))
## rep(is.ordered(x), ncol(x))
## else is.ordered(x)
## labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
## isord <- unlist(lapply(m[labs], tfun))
## storage.mode(X) <- "double"
## storage.mode(wt) <- "double"
## temp <- as.double(unlist(init$parms))
## if (!length(temp))
## temp <- 0
## rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord),
## method = as.integer(method.int), as.double(unlist(controls)),
## temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)),
## X, wt, as.integer(init$numy), as.double(cost))
## nsplit <- nrow(rpfit$isplit)
## ncat <- if (!is.null(rpfit$csplit))
## nrow(rpfit$csplit)
## else 0
## if (nsplit == 0)
## xval <- 0
## numcp <- ncol(rpfit$cptable)
## temp <- if (nrow(rpfit$cptable) == 3)
## c("CP", "nsplit", "rel error")
## else c("CP", "nsplit", "rel error", "xerror", "xstd")
## dimnames(rpfit$cptable) <- list(temp, 1:numcp)
## tname <- c("<leaf>", colnames(X))
## splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5,
## dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count",
## "ncat", "improve", "index", "adj")))
## index <- rpfit$inode[, 2]
## nadd <- sum(isord[rpfit$isplit[, 1]])
## if (nadd > 0) {
## newc <- matrix(0, nadd, max(cats))
## cvar <- rpfit$isplit[, 1]
## indx <- isord[cvar]
## cdir <- splits[indx, 2]
## ccut <- floor(splits[indx, 4])
## splits[indx, 2] <- cats[cvar[indx]]
## splits[indx, 4] <- ncat + 1:nadd
## for (i in 1:nadd) {
## newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
## newc[i, 1:ccut[i]] <- as.integer(cdir[i])
## }
## catmat <- if (ncat == 0)
## newc
## else {
## cs <- rpfit$csplit
## ncs <- ncol(cs)
## ncc <- ncol(newc)
## if (ncs < ncc)
## cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
## rbind(cs, newc)
## }
## ncat <- ncat + nadd
## }
## else catmat <- rpfit$csplit
## if (nsplit == 0) {
## frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[,
## 5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1],
## yval = rpfit$dnode[, 4], complexity = rpfit$dnode[,
## 2], ncompete = 0, nsurrogate = 0)
## }
## else {
## temp <- ifelse(index == 0, 1, index)
## svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
## frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar +
## 1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3],
## dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4],
## complexity = rpfit$dnode[, 2], ncompete = pmax(0,
## rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[,
## 4])
## }
## if (method.int == 3) {
## numclass <- init$numresp - 2
## nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
## temp <- pmax(1, init$counts)
## temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
## yprob <- temp/rowSums(temp)
## yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass +
## 1)
## frame$yval2 <- cbind(yval2, yprob, nodeprob)
## }
## else if (init$numresp > 1)
## frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
## if (is.null(init$summary))
## stop("Initialization routine is missing the 'summary' function")
## functions <- if (is.null(init$print))
## list(summary = init$summary)
## else list(summary = init$summary, print = init$print)
## if (!is.null(init$text))
## functions <- c(functions, list(text = init$text))
## if (method == "user")
## functions <- c(functions, mlist)
## where <- rpfit$which
## names(where) <- row.names(m)
## ans <- list(frame = frame, where = where, call = Call, terms = Terms,
## cptable = t(rpfit$cptable), method = method, parms = init$parms,
## control = controls, functions = functions, numresp = init$numresp)
## if (nsplit)
## ans$splits = splits
## if (ncat > 0)
## ans$csplit <- catmat + 2
## if (nsplit)
## ans$variable.importance <- importance(ans)
## if (model) {
## ans$model <- m
## if (missing(y))
## y <- FALSE
## }
## if (y)
## ans$y <- Y
## if (x) {
## ans$x <- X
## ans$wt <- wt
## }
## ans$ordered <- isord
## if (!is.null(attr(m, "na.action")))
## ans$na.action <- attr(m, "na.action")
## if (!is.null(xlevels))
## attr(ans, "xlevels") <- xlevels
## if (method == "class")
## attr(ans, "ylevels") <- init$ylevels
## class(ans) <- "rpart"
## ans
## })(formula = .outcome ~ ., data = list(c(1, 2, 3, 4, 5, 6, 7,
## 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 26, 27, 28,
## 29, 31, 33, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49,
## 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68,
## 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
## 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
## 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,
## 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 129,
## 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
## 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 157,
## 159, 160, 162, 163, 164, 165, 166, 168, 170, 171, 172, 173, 175,
## 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190,
## 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
## 204, 206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218,
## 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
## 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
## 247, 248, 250, 251, 253, 254, 255, 256, 257, 258, 259, 260, 261,
## 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274,
## 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288,
## 289, 290, 292, 293, 294, 295, 297, 300, 301, 302, 303, 304, 305,
## 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318,
## 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,
## 333, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347,
## 348, 349, 350, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362,
## 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375,
## 376, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 390, 391,
## 392, 394, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 409,
## 410, 411, 412, 413, 414, 416, 417, 418, 419, 420, 421, 422, 423,
## 424, 425, 426, 428, 429, 431, 432, 433, 434, 435, 437, 438, 440,
## 441, 442, 443, 444, 446, 448, 449, 450, 451, 453, 454, 455, 456,
## 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,
## 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483,
## 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496,
## 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 510,
## 511, 512, 513, 514, 515, 516, 517, 520, 521, 522, 524, 525, 527,
## 528, 529, 532, 533, 534, 535, 536, 538, 539, 540, 543, 544, 545,
## 546, 547, 548, 549, 550, 551, 553, 554, 555, 557, 558, 559, 560,
## 561, 562, 563, 565, 567, 568, 569, 570, 572, 573, 574, 575, 576,
## 577, 578, 581, 582, 583, 584, 585, 586, 587, 588, 590, 591, 592,
## 594, 595, 596, 597, 599, 600, 602, 603, 604, 605, 606, 608, 609,
## 610, 611, 612, 613, 615, 616, 617, 618, 619, 620, 621, 622, 623,
## 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 637, 639,
## 640, 641, 642, 643, 644, 645, 646, 647, 650, 651, 653, 654, 655,
## 656, 657, 658, 659, 660, 661, 662, 663, 664, 667, 668, 669, 670,
## 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684,
## 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698,
## 700, 701, 705, 706, 707, 708, 709, 711, 712, 714, 715, 716, 717,
## 718, 719, 720, 721, 722, 723, 724, 725, 726, 728, 729, 730, 731,
## 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 745,
## 746, 747, 749, 750, 751, 753, 755, 756, 757, 758, 759, 760, 761,
## 762, 764, 765, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776,
## 777, 778, 779, 780, 781, 782), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), c(19, 18, 19,
## 18, 18, 18, 18, 19, 20, 19, 18, 19, 18, 18, 18, 18, 18, 18, 18,
## 18, 18, 19, 18, 19, 20, 18, 19, 18, 18, 19, 19, 18, 19, 19, 18,
## 18, 19, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 19, 19, 19, 19,
## 18, 18, 18, 20, 24, 19, 19, 20, 18, 20, 19, 19, 20, 20, 19, 18,
## 18, 20, 20, 18, 19, 18, 18, 18, 19, 18, 19, 18, 20, 19, 18, 18,
## 19, 18, 19, 18, 18, 18, 18, 19, 19, 18, 19, 19, 18, 18, 18, 18,
## 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 18, 18,
## 18, 20, 21, 18, 19, 19, 18, 19, 19, 19, 18, 18, 23, 19, 18, 19,
## 18, 20, 18, 19, 19, 18, 18, 20, 18, 18, 18, 20, 19, 19, 18, 19,
## 18, 19, 18, 20, 18, 18, 18, 18, 20, 18, 18, 19, 18, 19, 23, 21,
## 20, 20, 26, 20, 19, 20, 19, 20, 19, 19, 19, 23, 19, 19, 19, 20,
## 18, 19, 21, 20, 19, 19, 20, 20, 24, 20, 20, 20, 21, 20, 19, 19,
## 18, 24, 19, 19, 19, 19, 21, 19, 19, 18, 19, 23, 21, 22, 20, 20,
## 20, 19, 19, 19, 19, 21, 19, 19, 19, 20, 20, 18, 21, 19, 20, 19,
## 20, 19, 19, 19, 19, 19, 21, 20, 19, 20, 24, 22, 19, 20, 21, 19,
## 20, 21, 19, 21, 20, 25, 19, 20, 20, 19, 20, 23, 20, 20, 19, 20,
## 20, 20, 19, 23, 20, 19, 19, 20, 19, 19, 19, 19, 19, 19, 18, 19,
## 19, 19, 19, 19, 20, 19, 20, 24, 19, 19, 19, 20, 21, 22, 19, 19,
## 23, 19, 19, 18, 20, 19, 19, 19, 20, 19, 20, 20, 19, 21, 19, 19,
## 19, 22, 22, 19, 22, 20, 19, 19, 21, 22, 19, 20, 18, 20, 19, 20,
## 20, 19, 19, 19, 21, 19, 18, 20, 21, 19, 19, 20, 19, 19, 19, 20,
## 18, 22, 19, 19, 20, 19, 19, 19, 20, 19, 20, 19, 20, 20, 19, 20,
## 20, 19, 21, 19, 23, 19, 19, 20, 20, 19, 24, 19, 21, 20, 19, 19,
## 19, 19, 21, 19, 19, 20, 20, 19, 20, 19, 19, 19, 19, 19, 19, 23,
## 19, 19, 19, 20, 20, 19, 19, 20, 19, 20, 20, 19, 25, 20, 21, 19,
## 21, 20, 19, 20, 19, 20, 20, 20, 21, 20, 20, 20, 22, 20, 20, 20,
## 21, 22, 20, 20, 21, 20, 20, 21, 22, 26, 20, 20, 20, 20, 20, 20,
## 21, 21, 20, 20, 20, 20, 20, 21, 20, 21, 20, 28, 20, 21, 20, 21,
## 20, 21, 22, 23, 21, 23, 19, 22, 21, 21, 20, 19, 20, 22, 21, 20,
## 20, 20, 21, 20, 21, 20, 21, 21, 21, 22, 20, 22, 20, 20, 20, 21,
## 23, 23, 22, 21, 21, 21, 21, 21, 21, 21, 22, 21, 22, 23, 22, 21,
## 22, 22, 21, 22, 18, 21, 22, 22, 25, 21, 22, 21, 21, 21, 21, 21,
## 23, 21, 21, 20, 27, 21, 22, 22, 22, 23, 21, 23, 22, 22, 21, 24,
## 21, 22, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 21, 21, 21,
## 21, 22, 22, 22, 21, 22, 24, 22, 21, 22, 21, 24, 21, 21, 21, 22,
## 21, 22, 21, 21, 22, 22, 25, 21, 24, 22, 22, 21, 21, 21, 21, 22,
## 22, 22, 22, 26, 21, 21, 22, 21, 21, 21, 22, 21, 22, 22, 21, 22,
## 22, 21, 22, 20, 21, 21, 21, 21, 20, 23, 21, 22, 21, 21, 21, 21,
## 21, 21, 21, 21, 21, 21, 22, 21, 21, 22, 21, 21, 21, 21, 23, 22,
## 21, 21, 24, 22, 22, 22, 22, 22, 22, 26, 22, 22, 21, 21, 22, 22,
## 30, 23, 22, 22, 22, 23, 24, 23, 22, 23, 22, 22, 24, 22, 22, 22,
## 23, 24, 24, 23, 22, 22, 21, 22, 23, 22, 23, 22, 23, 22, 24, 22,
## 22, 22), c(1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
## 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,
## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,
## 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,
## 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0,
## 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,
## 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,
## 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,
## 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,
## 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,
## 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1,
## 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,
## 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,
## 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,
## 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1,
## 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1,
## 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,
## 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
## 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
## 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
## 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0,
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
## 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1,
## 0, 0, 1, 1, 1, 0, 1, 1, 0), c(0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,
## 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
## 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
## 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,
## 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1,
## 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0,
## 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,
## 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
## 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
## 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0,
## 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
## 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,
## 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1,
## 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0,
## 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1,
## 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
## 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1), c(0, 0, 1, 0, 1,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,
## 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
## 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,
## 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,
## 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
## 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
## 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
## 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(0, 1, 1, 0, 0, 0, 0, 1, 1, 0,
## 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1,
## 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,
## 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
## 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,
## 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
## 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1,
## 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
## 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
## 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,
## 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
## 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,
## 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
## 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,
## 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,
## 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,
## 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,
## 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
## 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
## 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0), c(1, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
## 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
## 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,
## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
## 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
## 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
## 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0,
## 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0), c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
## 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,
## 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
## 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 1, 1,
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
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## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
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## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,
## 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1), c(0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
## 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
## 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
## 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), c(0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(1,
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
## 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
## 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,
## 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0,
## 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
## 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,
## 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,
## 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,
## 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
## 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
## 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 0), c(2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2,
## 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1,
## 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2,
## 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2,
## 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2,
## 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2,
## 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2,
## 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1,
## 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2,
## 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1,
## 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,
## 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,
## 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1,
## 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1,
## 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2,
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2,
## 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2,
## 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1,
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1,
## 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2,
## 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2,
## 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2,
## 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
## 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2,
## 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2,
## 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2,
## 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1,
## 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2,
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2,
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 1)), control = list(20, 7, 0, 4, 5,
## 2, 0, 30, 0))
## n= 677
##
## CP nsplit rel error
## 1 0.6701031 0 1.0000000
## 2 0.2164948 1 0.3298969
##
## Variable importance
## depression_severityModerate phq_score(9,14]
## 49 49
## bmi(39.9,100] who_bmiClass III Obesity
## 1 1
##
## Node number 1: 677 observations, complexity param=0.6701031
## predicted class=0 expected loss=0.2865583 P(node) =1
## class counts: 194 483
## probabilities: 0.287 0.713
## left son=2 (130 obs) right son=3 (547 obs)
## Primary splits:
## phq_score(9,14] < 0.5 to the right, improve=163.79160, (0 missing)
## depression_severityModerate < 0.5 to the right, improve=163.79160, (0 missing)
## suicidal0 < 0.5 to the left, improve= 69.47878, (0 missing)
## phq_score(4,9] < 0.5 to the left, improve= 67.47916, (0 missing)
## anxiousness0 < 0.5 to the left, improve= 60.48914, (0 missing)
## Surrogate splits:
## depression_severityModerate < 0.5 to the right, agree=1.000, adj=1.000, (0 split)
## bmi(39.9,100] < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
## who_bmiClass III Obesity < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
##
## Node number 2: 130 observations
## predicted class=1 expected loss=0 P(node) =0.1920236
## class counts: 130 0
## probabilities: 1.000 0.000
##
## Node number 3: 547 observations
## predicted class=0 expected loss=0.1170018 P(node) =0.8079764
## class counts: 64 483
## probabilities: 0.117 0.883
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.949398066993609" "Accuracy: 0.949398066993609"
## [3] "Accuracy: 0.826795397343992"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.908530510443737"
tree1 <- valuation_df <- data.frame(tree = "tree2", accuracy = accuracy, Atrribute="gini",
cv_K=5)
evaluation_df <- rbind(evaluation_df, tree1)
# visulize the tree
fancyRpartPlot(trained_model, caption = NULL)
folds <- createFolds(dataset$depressiveness, k = 5) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
# Compute the gain ratio of attributes for predicting the depressiveness
weights <- gain.ratio(depressiveness ~ ., data = dataset)
# Print the weights
print(weights)
## attr_importance
## id 0.000000000
## school_year 0.000000000
## age 0.000000000
## gender 0.004677615
## bmi 0.011351363
## who_bmi 0.011051916
## phq_score 0.404717233
## depression_severity 0.404717233
## suicidal 0.413593744
## depression_diagnosis 0.070324952
## depression_treatment 0.046224308
## gad_score 0.108969343
## anxiety_severity 0.108969343
## anxiousness 0.178526729
## anxiety_diagnosis 0.056732232
## anxiety_treatment 0.040011292
## epworth_score 0.054659804
## sleepiness 0.040786810
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = weights))
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.28655835 0.71344165)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.00000000 0.00000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.11700183 0.88299817)
## 6) suicidal0< 0.5 42 0 1 (1.00000000 0.00000000) *
## 7) suicidal0>=0.5 505 22 0 (0.04356436 0.95643564) *
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 163.791596 163.791596
## suicidal0 depression_severitySevere
## 70.940598 10.134371
## phq_score(19,27] epworth_score(17,24]
## 10.134371 6.756247
## bmi(39.9,100] who_bmiClass III Obesity
## 2.519871 2.519871
## bmi(34.9,39.9] who_bmiClass II Obesity
## 1.689062 1.689062
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.963440616769501" "Accuracy: 0.938671670373938"
## [3] "Accuracy: 0.826565827539792"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.909559371561077"
tree1 <- valuation_df <- data.frame(tree = "tree3", accuracy = accuracy, Atrribute="Gain ratio",
cv_K=5)
evaluation_df <- rbind(evaluation_df, tree1)
# visulize the tree
fancyRpartPlot(trained_model, caption = NULL)
evaluation_df
## tree accuracy Atrribute cv_K
## 1 tree1 0.8942690 information gain 5
## 2 tree2 0.9085305 gini 5
## 3 tree3 0.9095594 Gain ratio 5
folds <- createFolds(dataset$depressiveness, k = 7) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
train_control
## $method
## [1] "cv"
##
## $number
## [1] 10
##
## $repeats
## [1] NA
##
## $search
## [1] "grid"
##
## $p
## [1] 0.75
##
## $initialWindow
## NULL
##
## $horizon
## [1] 1
##
## $fixedWindow
## [1] TRUE
##
## $skip
## [1] 0
##
## $verboseIter
## [1] FALSE
##
## $returnData
## [1] TRUE
##
## $returnResamp
## [1] "final"
##
## $savePredictions
## [1] FALSE
##
## $classProbs
## [1] FALSE
##
## $summaryFunction
## function (data, lev = NULL, model = NULL)
## {
## if (is.character(data$obs))
## data$obs <- factor(data$obs, levels = lev)
## postResample(data[, "pred"], data[, "obs"])
## }
## <bytecode: 0x7fccf1684668>
## <environment: namespace:caret>
##
## $selectionFunction
## [1] "best"
##
## $preProcOptions
## $preProcOptions$thresh
## [1] 0.95
##
## $preProcOptions$ICAcomp
## [1] 3
##
## $preProcOptions$k
## [1] 5
##
## $preProcOptions$freqCut
## [1] 19
##
## $preProcOptions$uniqueCut
## [1] 10
##
## $preProcOptions$cutoff
## [1] 0.9
##
##
## $sampling
## NULL
##
## $index
## $index$Fold1
## [1] 6 14 21 22 23 28 35 47 55 68 73 83 88 91 92 97 99 107 124
## [20] 125 126 127 160 170 184 186 202 214 220 230 233 248 250 251 269 273 281 284
## [39] 286 290 292 296 301 310 319 321 346 365 382 383 386 390 393 396 402 414 418
## [58] 425 430 449 454 459 479 480 488 490 497 501 506 524 537 541 542 543 565 570
## [77] 576 584 589 590 591 592 593 601 606 610 615 617 619 624 640 643 658 667 671
## [96] 674
##
## $index$Fold2
## [1] 1 9 10 18 20 32 41 45 52 54 58 62 64 66 69 76 78 105 108
## [20] 109 110 112 128 136 145 146 147 150 154 165 166 175 190 198 209 216 240 242
## [39] 245 249 272 294 307 308 309 328 329 342 343 350 355 363 364 385 389 391 403
## [58] 412 415 423 424 426 439 443 444 446 450 455 457 472 475 486 487 525 531 545
## [77] 550 551 553 555 561 566 567 572 580 595 599 602 608 609 611 614 630 635 638
## [96] 657 666
##
## $index$Fold3
## [1] 5 13 27 46 56 63 72 81 95 98 103 114 130 132 138 161 162 164 167
## [20] 169 173 176 187 189 195 196 210 213 217 221 229 231 234 241 243 255 259 260
## [39] 261 262 282 283 285 288 289 313 315 327 339 353 354 356 359 370 374 384 387
## [58] 388 409 427 428 438 453 460 461 469 471 494 499 500 508 509 522 527 529 536
## [77] 539 544 552 554 563 569 583 598 605 607 618 621 622 623 628 637 641 644 655
## [96] 664 668
##
## $index$Fold4
## [1] 17 19 24 25 30 33 34 43 51 59 71 74 79 84 87 89 94 106 117
## [20] 121 134 141 143 149 151 158 171 177 179 181 182 188 199 200 205 212 222 223
## [39] 239 246 253 258 263 277 278 287 300 306 314 318 320 323 324 347 351 357 367
## [58] 371 372 377 378 380 404 405 408 417 420 421 422 429 434 436 440 452 476 477
## [77] 482 492 496 512 514 528 535 548 586 603 620 626 629 645 647 654 656 660 661
## [96] 662 677
##
## $index$Fold5
## [1] 7 12 29 31 42 50 60 75 77 96 100 101 102 133 137 142 159 168 174
## [20] 180 185 197 203 211 215 219 225 227 235 238 266 267 270 274 275 279 293 295
## [39] 298 302 311 312 317 322 332 334 337 341 345 362 373 399 400 401 407 410 413
## [58] 419 435 458 463 468 478 481 483 493 495 502 510 513 515 516 518 526 530 532
## [77] 538 562 564 571 575 578 582 588 597 600 604 613 625 631 648 650 651 669 672
## [96] 673
##
## $index$Fold6
## [1] 3 11 15 39 40 44 53 61 65 82 85 115 118 120 122 131 144 152 153
## [20] 172 178 183 192 193 194 201 204 207 218 224 228 232 236 237 252 254 256 257
## [39] 268 280 291 297 299 304 316 325 326 331 338 348 349 361 366 368 369 375 376
## [58] 379 394 432 437 442 445 447 451 464 465 466 467 470 484 485 489 498 504 520
## [77] 523 540 546 547 558 560 577 585 587 594 612 616 632 633 634 652 653 663 670
## [96] 675 676
##
## $index$Fold7
## [1] 2 4 8 16 26 36 37 38 48 49 57 67 70 80 86 90 93 104 111
## [20] 113 116 119 123 129 135 139 140 148 155 156 157 163 191 206 208 226 244 247
## [39] 264 265 271 276 303 305 330 333 335 336 340 344 352 358 360 381 392 395 397
## [58] 398 406 411 416 431 433 441 448 456 462 473 474 491 503 505 507 511 517 519
## [77] 521 533 534 549 556 557 559 568 573 574 579 581 596 627 636 639 642 646 649
## [96] 659 665
##
##
## $indexOut
## NULL
##
## $indexFinal
## NULL
##
## $timingSamps
## [1] 0
##
## $predictionBounds
## [1] FALSE FALSE
##
## $seeds
## [1] NA
##
## $adaptive
## $adaptive$min
## [1] 5
##
## $adaptive$alpha
## [1] 0.05
##
## $adaptive$method
## [1] "gls"
##
## $adaptive$complete
## [1] TRUE
##
##
## $trim
## [1] FALSE
##
## $allowParallel
## [1] TRUE
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = "information"))
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.2865583 0.7134417)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.0000000 0.0000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.1170018 0.8829982) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 208.133991 208.133991
## bmi(39.9,100] who_bmiClass III Obesity
## 3.202061 3.202061
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.936716888666559" "Accuracy: 0.936716888666559"
## [3] "Accuracy: 0.793983958352764"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.889139245228627"
res <- valuation_df <- data.frame(tree = "tree4", accuracy = accuracy, Atrribute="information gain",
cv_K=7)
evaluation_df <- rbind(evaluation_df, res)
folds <- createFolds(dataset$depressiveness, k = 7) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = "gini"))
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.28655835 0.71344165)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.00000000 0.00000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.11700183 0.88299817)
## 6) suicidal0< 0.5 42 0 1 (1.00000000 0.00000000) *
## 7) suicidal0>=0.5 505 22 0 (0.04356436 0.95643564) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 163.791596 163.791596
## suicidal0 depression_severitySevere
## 70.940598 10.134371
## phq_score(19,27] epworth_score(17,24]
## 10.134371 6.756247
## bmi(39.9,100] who_bmiClass III Obesity
## 2.519871 2.519871
## bmi(34.9,39.9] who_bmiClass II Obesity
## 1.689062 1.689062
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.94018042613805" "Accuracy: 0.9369784556947"
## [3] "Accuracy: 0.766397751456212"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.881185544429654"
res <- valuation_df <- data.frame(tree = "tree5", accuracy = accuracy, Atrribute="Gini index",
cv_K=7)
evaluation_df <- rbind(evaluation_df, res)
folds <- createFolds(dataset$depressiveness, k = 7) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
# Compute the gain ratio of attributes for predicting the depressiveness
weights <- gain.ratio(depressiveness ~ ., data = dataset)
# Print the weights
print(weights)
## attr_importance
## id 0.000000000
## school_year 0.000000000
## age 0.000000000
## gender 0.004677615
## bmi 0.011351363
## who_bmi 0.011051916
## phq_score 0.404717233
## depression_severity 0.404717233
## suicidal 0.413593744
## depression_diagnosis 0.070324952
## depression_treatment 0.046224308
## gad_score 0.108969343
## anxiety_severity 0.108969343
## anxiousness 0.178526729
## anxiety_diagnosis 0.056732232
## anxiety_treatment 0.040011292
## epworth_score 0.054659804
## sleepiness 0.040786810
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = weights))
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = weights))
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.2865583 0.7134417)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.0000000 0.0000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.1170018 0.8829982) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 163.791596 163.791596
## bmi(39.9,100] who_bmiClass III Obesity
## 2.519871 2.519871
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.945599569283467" "Accuracy: 0.945599569283467"
## [3] "Accuracy: 0.793198833334746"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.894799323967227"
res <- valuation_df <- data.frame(tree = "tree6", accuracy = accuracy, Atrribute="Gain Ratio",
cv_K=7)
evaluation_df <- rbind(evaluation_df, res)
fancyRpartPlot(trained_model, caption = NULL)
folds <- createFolds(dataset$depressiveness, k = 10) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = "information"))
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.2865583 0.7134417)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.0000000 0.0000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.1170018 0.8829982) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 208.133991 208.133991
## bmi(39.9,100] who_bmiClass III Obesity
## 3.202061 3.202061
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.91334268755552" "Accuracy: 0.91663216123973"
## [3] "Accuracy: 0.827798171587816"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.885924340127689"
res <- valuation_df <- data.frame(tree = "tree7", accuracy = accuracy, Atrribute="information gain",
cv_K=10)
evaluation_df <- rbind(evaluation_df, res)
folds <- createFolds(dataset$depressiveness, k = 10) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = "gini"))
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.28655835 0.71344165)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.00000000 0.00000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.11700183 0.88299817)
## 6) suicidal0< 0.5 42 0 1 (1.00000000 0.00000000) *
## 7) suicidal0>=0.5 505 22 0 (0.04356436 0.95643564) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 163.791596 163.791596
## suicidal0 depression_severitySevere
## 70.940598 10.134371
## phq_score(19,27] epworth_score(17,24]
## 10.134371 6.756247
## bmi(39.9,100] who_bmiClass III Obesity
## 2.519871 2.519871
## bmi(34.9,39.9] who_bmiClass II Obesity
## 1.689062 1.689062
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.893627284718297" "Accuracy: 0.880196236776225"
## [3] "Accuracy: 0.807499259737813"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.860440927077445"
res <- valuation_df <- data.frame(tree = "tree8", accuracy = accuracy, Atrribute="Gini index",
cv_K=10)
evaluation_df <- rbind(evaluation_df, res)
folds <- createFolds(dataset$depressiveness, k = 10) # create the folds
# control parameters
train_control <- trainControl(method = "cv", index = folds)
# Compute the gain ratio of attributes for predicting the depressiveness
weights <- gain.ratio(depressiveness ~ ., data = dataset)
# Print the weights
print(weights)
## attr_importance
## id 0.000000000
## school_year 0.000000000
## age 0.000000000
## gender 0.004677615
## bmi 0.011351363
## who_bmi 0.011051916
## phq_score 0.404717233
## depression_severity 0.404717233
## suicidal 0.413593744
## depression_diagnosis 0.070324952
## depression_treatment 0.046224308
## gad_score 0.108969343
## anxiety_severity 0.108969343
## anxiousness 0.178526729
## anxiety_diagnosis 0.056732232
## anxiety_treatment 0.040011292
## epworth_score 0.054659804
## sleepiness 0.040786810
#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control,
method = "rpart", parms = list(split = weights))
# train result
trained_model <- tree$finalModel
trained_model
## n= 677
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 677 194 0 (0.28655835 0.71344165)
## 2) phq_score(9,14]>=0.5 130 0 1 (1.00000000 0.00000000) *
## 3) phq_score(9,14]< 0.5 547 64 0 (0.11700183 0.88299817)
## 6) suicidal0< 0.5 42 0 1 (1.00000000 0.00000000) *
## 7) suicidal0>=0.5 505 22 0 (0.04356436 0.95643564) *
#features importance
trained_model$variable.importance
## depression_severityModerate phq_score(9,14]
## 163.791596 163.791596
## suicidal0 depression_severitySevere
## 70.940598 10.134371
## phq_score(19,27] epworth_score(17,24]
## 10.134371 6.756247
## bmi(39.9,100] who_bmiClass III Obesity
## 2.519871 2.519871
## bmi(34.9,39.9] who_bmiClass II Obesity
## 1.689062 1.689062
#accuracy of the tree
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))
## [1] "Accuracy: 0.907600960782397" "Accuracy: 0.905466313820164"
## [3] "Accuracy: 0.808289539209811"
# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))
## [1] "Average Accuracy: 0.873785604604124"
res <- valuation_df <- data.frame(tree = "tree9", accuracy = accuracy, Atrribute="Gain Ratio",
cv_K=10)
evaluation_df <- rbind(evaluation_df, res)
evaluation_df
## tree accuracy Atrribute cv_K
## 1 tree1 0.8942690 information gain 5
## 2 tree2 0.9085305 gini 5
## 3 tree3 0.9095594 Gain ratio 5
## 4 tree4 0.8891392 information gain 7
## 5 tree5 0.8811855 Gini index 7
## 6 tree6 0.8947993 Gain Ratio 7
## 7 tree7 0.8859243 information gain 10
## 8 tree8 0.8604409 Gini index 10
## 9 tree9 0.8737856 Gain Ratio 10
# include the libraries and import the dataset
library('superml')
## Loading required package: R6
library(cluster)
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
# import the dataset
df<-dataset
head(df,10)
## # A tibble: 10 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <chr> <fct> <chr> <fct> <chr>
## 1 1 1 19 male (29.9,3… Class … (4,9] Mild
## 2 2 1 18 male (18.4,2… Normal (4,9] Mild
## 3 3 1 19 male (24.9,2… Overwe… (4,9] Mild
## 4 4 1 18 female (18.4,2… Normal (14,19] Moderately severe
## 5 5 1 18 male (24.9,2… Overwe… (4,9] Mild
## 6 6 1 18 male (18.4,2… Normal (0,4] None-minimal
## 7 7 1 18 male (18.4,2… Normal (4,9] Mild
## 8 8 1 19 male (18.4,2… Normal (0,4] None-minimal
## 9 9 1 20 male (18.4,2… Normal (9,14] Moderate
## 10 10 1 19 male (18.4,2… Normal (4,9] Mild
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## # depression_diagnosis <fct>, depression_treatment <fct>, gad_score <fct>,
## # anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## # anxiety_treatment <fct>, epworth_score <fct>, sleepiness <fct>
str(df)
## tibble [677 × 19] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:677] 1 2 3 4 5 6 7 8 9 10 ...
## $ school_year : num [1:677] 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num [1:677] 19 18 19 18 18 18 18 19 20 19 ...
## $ gender : chr [1:677] "male" "male" "male" "female" ...
## $ bmi : Factor w/ 6 levels "(18,18.4]","(18.4,24.9]",..: 4 2 3 2 3 2 2 2 2 2 ...
## $ who_bmi : chr [1:677] "Class I Obesity" "Normal" "Overweight" "Normal" ...
## $ phq_score : Factor w/ 5 levels "(0,4]","(4,9]",..: 2 2 2 4 2 1 2 1 3 2 ...
## $ depression_severity : chr [1:677] "Mild" "Mild" "Mild" "Moderately severe" ...
## $ depressiveness : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
## $ suicidal : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 2 2 ...
## $ depression_diagnosis: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ depression_treatment: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ gad_score : Factor w/ 4 levels "(0,4]","(4,9]",..: 3 2 2 4 3 1 1 2 2 1 ...
## $ anxiety_severity : chr [1:677] "Moderate" "Mild" "Mild" "Severe" ...
## $ anxiousness : Factor w/ 2 levels "1","0": 1 2 2 1 1 2 2 2 2 2 ...
## $ anxiety_diagnosis : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ anxiety_treatment : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ epworth_score : Factor w/ 4 levels "(0,10]","(10,14]",..: 1 2 1 2 1 1 1 1 1 1 ...
## $ sleepiness : Factor w/ 2 levels "1","0": 2 1 2 1 2 2 2 2 2 2 ...
## - attr(*, "na.action")= 'omit' Named int [1:77] 18 27 30 32 55 56 67 118 124 147 ...
## ..- attr(*, "names")= chr [1:77] "18" "27" "30" "32" ...
# using label encoder
encoding <- LabelEncoder$new()
# Identify categorical columns
categorical_cols <- dataset %>%
select_if(is.character) %>%
names()
categorical_cols
## [1] "gender" "who_bmi" "depression_severity"
## [4] "anxiety_severity"
# Convert categorical columns to factors
df[categorical_cols] <- lapply(df[categorical_cols], as.factor)
str(df)
## tibble [677 × 19] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:677] 1 2 3 4 5 6 7 8 9 10 ...
## $ school_year : num [1:677] 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num [1:677] 19 18 19 18 18 18 18 19 20 19 ...
## $ gender : Factor w/ 2 levels "female","male": 2 2 2 1 2 2 2 2 2 2 ...
## $ bmi : Factor w/ 6 levels "(18,18.4]","(18.4,24.9]",..: 4 2 3 2 3 2 2 2 2 2 ...
## $ who_bmi : Factor w/ 6 levels "Class I Obesity",..: 1 4 5 4 5 4 4 4 4 4 ...
## $ phq_score : Factor w/ 5 levels "(0,4]","(4,9]",..: 2 2 2 4 2 1 2 1 3 2 ...
## $ depression_severity : Factor w/ 5 levels "Mild","Moderate",..: 1 1 1 3 1 4 1 4 2 1 ...
## $ depressiveness : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
## $ suicidal : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 2 2 ...
## $ depression_diagnosis: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ depression_treatment: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ gad_score : Factor w/ 4 levels "(0,4]","(4,9]",..: 3 2 2 4 3 1 1 2 2 1 ...
## $ anxiety_severity : Factor w/ 4 levels "Mild","Moderate",..: 2 1 1 4 2 3 3 1 1 3 ...
## $ anxiousness : Factor w/ 2 levels "1","0": 1 2 2 1 1 2 2 2 2 2 ...
## $ anxiety_diagnosis : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ anxiety_treatment : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
## $ epworth_score : Factor w/ 4 levels "(0,10]","(10,14]",..: 1 2 1 2 1 1 1 1 1 1 ...
## $ sleepiness : Factor w/ 2 levels "1","0": 2 1 2 1 2 2 2 2 2 2 ...
## - attr(*, "na.action")= 'omit' Named int [1:77] 18 27 30 32 55 56 67 118 124 147 ...
## ..- attr(*, "names")= chr [1:77] "18" "27" "30" "32" ...
categorical_cols <- dataset %>%
select_if(is.factor) %>%
names()
categorical_cols
## [1] "bmi" "phq_score" "depressiveness"
## [4] "suicidal" "depression_diagnosis" "depression_treatment"
## [7] "gad_score" "anxiousness" "anxiety_diagnosis"
## [10] "anxiety_treatment" "epworth_score" "sleepiness"
for (col in categorical_cols)
{
# Fit the LabelEncoder object to the data frame column
encoding$fit(df[[col]])
# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df[[col]])
# Assign the encoded column to the data frame
df[[col]] <- encoded_col
}
head(df,10)
## # A tibble: 10 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <fct> <dbl> <fct> <dbl> <fct>
## 1 1 1 19 male 3 Class I O… 1 Mild
## 2 2 1 18 male 1 Normal 1 Mild
## 3 3 1 19 male 2 Overweight 1 Mild
## 4 4 1 18 female 1 Normal 3 Moderately severe
## 5 5 1 18 male 2 Overweight 1 Mild
## 6 6 1 18 male 1 Normal 0 None-minimal
## 7 7 1 18 male 1 Normal 1 Mild
## 8 8 1 19 male 1 Normal 0 None-minimal
## 9 9 1 20 male 1 Normal 2 Moderate
## 10 10 1 19 male 1 Normal 1 Mild
## # ℹ 11 more variables: depressiveness <dbl>, suicidal <dbl>,
## # depression_diagnosis <dbl>, depression_treatment <dbl>, gad_score <dbl>,
## # anxiety_severity <fct>, anxiousness <dbl>, anxiety_diagnosis <dbl>,
## # anxiety_treatment <dbl>, epworth_score <dbl>, sleepiness <dbl>
# still some data has not changed lets convert them manually
encoding$fit(df$gender)
# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$gender)
# Assign the encoded column to the data frame
df$gender <- encoded_col
# still some data has not changed lets convert them manually
encoding$fit(df$depression_severity)
# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$depression_severity)
# Assign the encoded column to the data frame
df$depression_severity <- encoded_col
# still some data has not changed lets convert them manually
encoding$fit(df$who_bmi)
# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$who_bmi)
# Assign the encoded column to the data frame
df$who_bmi <- encoded_col
# still some data has not changed lets convert them manually
encoding$fit(df$anxiety_severity)
# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$anxiety_severity)
# Assign the encoded column to the data frame
df$anxiety_severity <- encoded_col
head(df)
## # A tibble: 6 × 19
## id school_year age gender bmi who_bmi phq_score depression_severity
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 19 1 3 0 1 0
## 2 2 1 18 1 1 3 1 0
## 3 3 1 19 1 2 4 1 0
## 4 4 1 18 0 1 3 3 2
## 5 5 1 18 1 2 4 1 0
## 6 6 1 18 1 1 3 0 3
## # ℹ 11 more variables: depressiveness <dbl>, suicidal <dbl>,
## # depression_diagnosis <dbl>, depression_treatment <dbl>, gad_score <dbl>,
## # anxiety_severity <dbl>, anxiousness <dbl>, anxiety_diagnosis <dbl>,
## # anxiety_treatment <dbl>, epworth_score <dbl>, sleepiness <dbl>
# lets spilt the data to target will not be used for clustering , also id not relvant to the case
target <- data.frame(depressiveness = df$depressiveness)
predictors <- df[, -c(1,9)]
head(predictors)
## # A tibble: 6 × 17
## school_year age gender bmi who_bmi phq_score depression_severity suicidal
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 19 1 3 0 1 0 1
## 2 1 18 1 1 3 1 0 1
## 3 1 19 1 2 4 1 0 1
## 4 1 18 0 1 3 3 2 0
## 5 1 18 1 2 4 1 0 1
## 6 1 18 1 1 3 0 3 1
## # ℹ 9 more variables: depression_diagnosis <dbl>, depression_treatment <dbl>,
## # gad_score <dbl>, anxiety_severity <dbl>, anxiousness <dbl>,
## # anxiety_diagnosis <dbl>, anxiety_treatment <dbl>, epworth_score <dbl>,
## # sleepiness <dbl>
# Define a function to compute BCubed precision and recall
bcubed <- function(cluster, category) {
# Check the input arguments
if (length(cluster) != length(category)) {
stop("cluster and category must have the same length")
}
if (any(is.na(cluster)) || any(is.na(category))) {
stop("cluster and category must not contain NA values")
}
# Convert cluster and category to factors
cluster <- factor(cluster)
category <- factor(category)
# Initialize the precision and recall vectors
precision <- numeric(length(cluster))
recall <- numeric(length(cluster))
# Loop over each item
for (i in 1:length(cluster)) {
# Find the items in the same cluster as the current item
same_cluster <- which(cluster == cluster[i])
# Find the items in the same category as the current item
same_category <- which(category == category[i])
# Compute the precision and recall for the current item
precision[i] <- length(intersect(same_cluster, same_category)) / length(same_cluster)
recall[i] <- length(intersect(same_cluster, same_category)) / length(same_category)
}
# Return the average precision and recall
return(c(precision = mean(precision), recall = mean(recall)))
}
fviz_nbclust(predictors, kmeans, method = "wss")
#perform k-means clustering with k = 5 clusters
km <- kmeans(predictors, centers = 5, nstart = 25)
#view results
km
## K-means clustering with 5 clusters of sizes 102, 74, 250, 88, 163
##
## Cluster means:
## school_year age gender bmi who_bmi phq_score
## 1 1.450980 19.12745 0.5686275 1.196078 2.941176 0.03921569
## 2 1.851351 19.81081 0.2972973 1.472973 3.270270 2.51351351
## 3 1.564000 19.06800 0.4640000 1.372000 3.108000 1.29600000
## 4 3.454545 21.71591 0.6590909 1.386364 3.465909 0.00000000
## 5 3.380368 22.01840 0.4171779 1.411043 3.220859 1.31288344
## depression_severity suicidal depression_diagnosis depression_treatment
## 1 3.0098039 0.9901961 0.9509804 0.9607843
## 2 1.6351351 0.6621622 0.8378378 0.8378378
## 3 0.2960000 0.9120000 0.9360000 0.9400000
## 4 3.0000000 0.9886364 0.9431818 0.9545455
## 5 0.3128834 0.9202454 0.8773006 0.9141104
## gad_score anxiety_severity anxiousness anxiety_diagnosis anxiety_treatment
## 1 0.2647059 1.500000 0.99019608 0.9705882 0.9607843
## 2 2.8108108 2.689189 0.01351351 0.8648649 0.8378378
## 3 0.9960000 0.812000 0.73200000 0.9360000 0.9160000
## 4 0.4204545 1.227273 0.97727273 0.9318182 0.9431818
## 5 1.0306748 0.607362 0.78527607 0.8957055 0.9325153
## epworth_score sleepiness
## 1 0.05882353 0.9313725
## 2 0.63513514 0.5405405
## 3 0.16400000 0.8000000
## 4 0.02272727 0.9431818
## 5 0.17177914 0.7791411
##
## Clustering vector:
## [1] 3 3 3 2 3 1 3 1 3 3 1 3 3 3 3 3 3 3 1 3 1 3 2 1 2 1 1 3 3 1 1 1 3 2 3 3 3
## [38] 1 3 1 1 3 3 3 3 1 3 3 3 3 3 1 3 1 3 5 3 3 2 1 1 3 3 1 3 1 3 1 3 1 1 3 3 3
## [75] 3 3 2 1 3 3 1 1 3 3 3 2 3 3 3 3 3 3 1 3 3 1 1 3 3 1 2 3 3 3 1 3 3 2 3 3 1
## [112] 2 2 3 3 3 3 3 1 2 1 2 3 3 2 3 3 2 3 3 1 3 1 2 3 3 3 3 2 3 3 3 1 2 1 3 1 3
## [149] 3 3 3 3 2 3 1 1 1 3 3 1 3 2 2 1 3 5 3 2 3 3 2 3 2 1 5 3 3 1 2 3 3 3 1 1 1
## [186] 3 3 5 2 1 1 3 3 3 1 3 2 3 1 3 1 1 2 2 2 2 5 3 5 1 2 2 3 1 2 1 1 3 1 3 3 1
## [223] 3 2 1 1 1 3 3 3 1 3 3 2 3 1 3 4 5 3 3 3 2 3 4 3 5 3 5 3 1 3 3 3 5 2 3 2 3
## [260] 2 3 1 4 3 3 3 3 2 3 2 3 3 2 1 3 3 3 2 2 2 3 1 5 3 3 3 3 5 5 3 3 4 3 1 3 3
## [297] 3 3 1 1 3 2 3 3 5 3 3 3 5 5 3 5 1 2 3 5 2 3 3 3 3 2 3 1 3 3 3 4 1 3 3 5 1
## [334] 3 3 2 3 1 1 2 4 1 3 2 2 3 3 3 3 3 3 1 2 3 3 1 3 4 3 5 3 3 1 3 3 5 3 1 3 3
## [371] 2 3 3 5 3 3 3 3 1 3 3 3 3 1 3 3 5 1 1 3 3 3 3 3 3 3 3 1 3 5 3 5 3 4 3 2 3
## [408] 1 3 3 3 5 1 1 3 2 3 4 3 5 5 3 4 5 3 3 5 5 5 4 1 1 3 1 1 4 5 3 1 1 1 3 4 3
## [445] 4 4 5 3 5 3 4 3 2 5 4 4 5 3 2 5 5 1 3 2 4 5 3 2 3 4 3 5 2 5 5 2 4 3 5 2 3
## [482] 2 5 5 5 4 4 4 5 4 5 5 5 5 4 5 5 5 4 4 5 4 5 1 4 5 5 4 5 4 5 4 4 5 5 5 5 5
## [519] 4 5 4 4 5 4 5 4 4 5 5 5 5 5 4 5 5 5 4 5 4 4 4 5 2 5 4 4 4 5 5 4 5 5 5 5 5
## [556] 5 4 5 5 5 5 5 4 5 5 5 5 5 5 4 4 5 5 5 4 5 5 5 4 5 2 4 5 4 5 4 5 5 5 5 5 4
## [593] 5 5 5 5 5 5 2 4 4 5 2 5 5 4 5 4 4 5 5 5 5 5 4 4 5 4 5 5 5 4 5 5 5 5 2 5 5
## [630] 5 5 4 4 5 5 4 4 5 4 4 5 4 4 5 5 4 5 2 4 4 4 5 5 4 5 5 5 2 5 4 5 4 5 5 5 5
## [667] 5 5 4 4 5 4 5 5 4 4 2
##
## Within cluster sum of squares by cluster:
## [1] 407.0294 663.9324 1258.0080 357.6023 1084.8221
## (between_SS / total_SS = 50.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
#plot results of k-means
fviz_cluster(km, data =predictors )
clusplot(df, km$cluster, color = TRUE, shade = TRUE, labels = 5)
# Evaluate and compare the results
silhouette_score <- silhouette(km$cluster, dist(predictors))
silhouette_score
## cluster neighbor sil_width
## [1,] 3 1 0.1985604728
## [2,] 3 1 0.3444898182
## [3,] 3 1 0.3748770391
## [4,] 2 3 0.2439215171
## [5,] 3 1 0.3399324144
## [6,] 1 3 0.5042503395
## [7,] 3 1 0.2459758945
## [8,] 1 3 0.3424330876
## [9,] 3 1 0.2811681553
## [10,] 3 1 0.2636563049
## [11,] 1 3 0.5042503395
## [12,] 3 1 0.2611952801
## [13,] 3 1 0.3444898182
## [14,] 3 2 0.2732914799
## [15,] 3 1 0.2290234647
## [16,] 3 1 0.3586856066
## [17,] 3 1 0.3768467060
## [18,] 3 1 0.2419436914
## [19,] 1 3 0.5042503395
## [20,] 3 2 0.2953145176
## [21,] 1 3 0.5042503395
## [22,] 3 1 0.3752158818
## [23,] 2 3 0.1413920667
## [24,] 1 3 0.5274816718
## [25,] 2 3 0.0275355183
## [26,] 1 3 0.5042503395
## [27,] 1 3 0.5274816718
## [28,] 3 1 0.2459758945
## [29,] 3 1 0.2459758945
## [30,] 1 3 0.5380517009
## [31,] 1 3 0.5380517009
## [32,] 1 3 0.2560003076
## [33,] 3 1 0.3797705414
## [34,] 2 3 0.0008967437
## [35,] 3 1 0.2909772331
## [36,] 3 1 0.3512394130
## [37,] 3 1 0.2729167424
## [38,] 1 3 0.5042503395
## [39,] 3 1 0.2539782152
## [40,] 1 3 0.4735298860
## [41,] 1 3 0.5042503395
## [42,] 3 2 0.3040905352
## [43,] 3 1 0.3524996061
## [44,] 3 1 0.3512394130
## [45,] 3 2 0.2038803160
## [46,] 1 3 0.2307334375
## [47,] 3 2 0.2155079536
## [48,] 3 1 0.2711047389
## [49,] 3 2 0.3516831161
## [50,] 3 1 0.2499180032
## [51,] 3 1 0.4013781652
## [52,] 1 3 0.2305104586
## [53,] 3 2 0.2732914799
## [54,] 1 3 0.2098164864
## [55,] 3 2 0.0864671669
## [56,] 5 4 0.1442341392
## [57,] 3 1 0.2941468410
## [58,] 3 1 0.2611952801
## [59,] 2 3 0.2050130862
## [60,] 1 3 0.5042503395
## [61,] 1 3 0.0427784984
## [62,] 3 1 0.1610987125
## [63,] 3 2 0.3231576371
## [64,] 1 4 0.4051345349
## [65,] 3 5 0.3223627799
## [66,] 1 3 0.3396748705
## [67,] 3 1 0.3697634665
## [68,] 1 3 0.3939388622
## [69,] 3 5 0.1877609450
## [70,] 1 4 0.2931543357
## [71,] 1 3 0.4433286060
## [72,] 3 2 0.2646893399
## [73,] 3 1 0.2539782152
## [74,] 3 1 0.2480756852
## [75,] 3 2 0.2732914799
## [76,] 3 2 0.2359900370
## [77,] 2 3 0.1344159563
## [78,] 1 3 0.5380517009
## [79,] 3 1 0.3567825245
## [80,] 3 2 0.2801391491
## [81,] 1 3 0.2512486289
## [82,] 1 3 0.5042503395
## [83,] 3 1 0.2480756852
## [84,] 3 1 0.2941823244
## [85,] 3 1 0.3697634665
## [86,] 2 3 -0.0058278610
## [87,] 3 1 0.3697634665
## [88,] 3 1 0.3500234273
## [89,] 3 1 0.3399324144
## [90,] 3 1 0.3768467060
## [91,] 3 1 0.3655206646
## [92,] 3 1 0.2611952801
## [93,] 1 3 0.5042503395
## [94,] 3 1 0.2711047389
## [95,] 3 1 0.2682299167
## [96,] 1 3 0.5042503395
## [97,] 1 3 0.5042503395
## [98,] 3 1 0.1960085716
## [99,] 3 1 0.2631486519
## [100,] 1 3 0.5042503395
## [101,] 2 3 0.1344159563
## [102,] 3 2 0.2680698314
## [103,] 3 1 0.2539782152
## [104,] 3 1 0.3527279837
## [105,] 1 3 0.5042503395
## [106,] 3 1 0.3512394130
## [107,] 3 1 0.3697634665
## [108,] 2 3 0.0093266457
## [109,] 3 1 0.3636593172
## [110,] 3 1 0.3636593172
## [111,] 1 3 0.5274816718
## [112,] 2 3 0.1882003002
## [113,] 2 3 0.1952269480
## [114,] 3 1 0.1282455920
## [115,] 3 1 0.1409445724
## [116,] 3 1 0.3527279837
## [117,] 3 1 0.1082063037
## [118,] 3 1 0.1107452175
## [119,] 1 3 0.5042503395
## [120,] 2 3 0.1819571768
## [121,] 1 3 0.5274816718
## [122,] 2 3 0.1344159563
## [123,] 3 1 0.2611952801
## [124,] 3 2 0.2066315145
## [125,] 2 3 0.1577437047
## [126,] 3 1 0.3527279837
## [127,] 3 1 0.3586856066
## [128,] 2 5 0.0938932795
## [129,] 3 2 0.2063348470
## [130,] 3 2 0.2680698314
## [131,] 1 3 0.3424330876
## [132,] 3 1 0.2539782152
## [133,] 1 4 0.4066316773
## [134,] 2 1 0.2050683212
## [135,] 3 1 0.2636563049
## [136,] 3 1 0.2621965225
## [137,] 3 2 0.2680698314
## [138,] 3 1 0.2539782152
## [139,] 2 3 0.2487151148
## [140,] 3 1 0.1188974491
## [141,] 3 1 0.2480756852
## [142,] 3 1 0.2283500308
## [143,] 1 4 0.2931543357
## [144,] 2 3 -0.0788622652
## [145,] 1 3 0.3396748705
## [146,] 3 1 0.2331320937
## [147,] 1 3 0.3396748705
## [148,] 3 1 0.3697634665
## [149,] 3 1 0.2025336673
## [150,] 3 1 0.2539782152
## [151,] 3 2 0.2137593889
## [152,] 3 1 0.2459758945
## [153,] 2 3 0.1074130055
## [154,] 3 2 0.1673818505
## [155,] 1 3 0.4735298860
## [156,] 1 4 0.3029655435
## [157,] 1 3 0.4056187447
## [158,] 3 1 0.3768467060
## [159,] 3 2 0.3340313438
## [160,] 1 3 0.4056187447
## [161,] 3 1 0.3864401712
## [162,] 2 5 0.0655289802
## [163,] 2 3 0.0370749947
## [164,] 1 3 0.1679791559
## [165,] 3 1 0.1397492592
## [166,] 5 4 0.1257851645
## [167,] 3 1 0.2435977210
## [168,] 2 3 0.2091074983
## [169,] 3 5 0.2787626282
## [170,] 3 1 0.2941823244
## [171,] 2 3 -0.0053691541
## [172,] 3 1 0.2711047389
## [173,] 2 3 0.0616490947
## [174,] 1 3 0.5274816718
## [175,] 5 3 0.1942571453
## [176,] 3 2 0.2295945435
## [177,] 3 2 0.1441166647
## [178,] 1 3 0.5274816718
## [179,] 2 3 0.3143614600
## [180,] 3 1 0.2515720963
## [181,] 3 1 0.3655206646
## [182,] 3 5 0.0890426972
## [183,] 1 4 0.2233523615
## [184,] 1 3 0.5380517009
## [185,] 1 3 0.5380517009
## [186,] 3 5 0.2777232672
## [187,] 3 5 0.3366946620
## [188,] 5 4 0.1137015166
## [189,] 2 1 -0.0221312931
## [190,] 1 4 0.1817631415
## [191,] 1 4 0.4066316773
## [192,] 3 5 0.0839855414
## [193,] 3 5 0.2956713144
## [194,] 3 2 0.3768116041
## [195,] 1 3 0.5274816718
## [196,] 3 1 0.3503668112
## [197,] 2 4 0.1047531808
## [198,] 3 1 0.3591811317
## [199,] 1 3 0.5274816718
## [200,] 3 1 0.2304663655
## [201,] 1 3 0.4677055904
## [202,] 1 4 0.0854769370
## [203,] 2 1 0.2515370487
## [204,] 2 3 0.0158560384
## [205,] 2 3 0.1104863529
## [206,] 2 3 0.2950152572
## [207,] 5 3 0.1554256631
## [208,] 3 5 0.1088073609
## [209,] 5 3 0.0599131045
## [210,] 1 4 0.3061966891
## [211,] 2 1 0.2845246041
## [212,] 2 3 0.2356408370
## [213,] 3 1 0.3591811317
## [214,] 1 3 0.2569265726
## [215,] 2 1 0.2221166139
## [216,] 1 3 0.5380517009
## [217,] 1 4 0.1487007690
## [218,] 3 1 0.3488642998
## [219,] 1 3 0.2760059690
## [220,] 3 2 0.3408499064
## [221,] 3 1 0.2278649498
## [222,] 1 4 0.2208826280
## [223,] 3 1 0.3768467060
## [224,] 2 3 0.1947380437
## [225,] 1 3 0.5274816718
## [226,] 1 4 0.2931543357
## [227,] 1 3 0.5380517009
## [228,] 3 5 0.3156058403
## [229,] 3 1 0.2907298042
## [230,] 3 2 0.2646893399
## [231,] 1 3 0.3398469199
## [232,] 3 2 0.1885393360
## [233,] 3 1 0.2711047389
## [234,] 2 3 0.0644255594
## [235,] 3 5 0.3358698428
## [236,] 1 3 0.3771297879
## [237,] 3 1 0.2398364840
## [238,] 4 1 0.2346658270
## [239,] 5 3 0.0368205489
## [240,] 3 1 0.1992134921
## [241,] 3 5 0.2915781933
## [242,] 3 5 0.1163863487
## [243,] 2 3 0.2768711161
## [244,] 3 5 0.2488546182
## [245,] 4 1 0.0888669700
## [246,] 3 5 0.4090472791
## [247,] 5 3 0.0123115762
## [248,] 3 5 0.2069547514
## [249,] 5 4 0.2084784276
## [250,] 3 5 0.3866198920
## [251,] 1 4 0.2626154838
## [252,] 3 1 0.1573408081
## [253,] 3 1 0.2526441555
## [254,] 3 5 0.2514523691
## [255,] 5 4 0.1261465938
## [256,] 2 3 0.2804384771
## [257,] 3 2 0.1624746783
## [258,] 2 3 0.2929575927
## [259,] 3 5 0.1973811902
## [260,] 2 3 0.3304621734
## [261,] 3 5 0.2264775926
## [262,] 1 3 0.3307375935
## [263,] 4 5 0.2093244687
## [264,] 3 5 0.2191718720
## [265,] 3 2 0.1629637788
## [266,] 3 1 0.4049351429
## [267,] 3 5 0.1495552780
## [268,] 2 3 0.2726407992
## [269,] 3 1 0.2748705778
## [270,] 2 3 0.3111520502
## [271,] 3 1 0.3212141340
## [272,] 3 2 0.2483661545
## [273,] 2 3 0.0231012041
## [274,] 1 3 0.2476190801
## [275,] 3 1 0.2023790157
## [276,] 3 2 0.2227568790
## [277,] 3 2 0.3722742721
## [278,] 2 3 0.0170276376
## [279,] 2 3 0.1969676079
## [280,] 2 3 0.2279700728
## [281,] 3 1 0.1615081622
## [282,] 1 4 0.1757549842
## [283,] 5 4 0.1703507472
## [284,] 3 5 0.3853469257
## [285,] 3 1 0.2841723975
## [286,] 3 2 0.3349018593
## [287,] 3 5 0.2488546182
## [288,] 5 3 0.0057790467
## [289,] 5 4 0.0925687364
## [290,] 3 5 0.4090472791
## [291,] 3 1 0.2943767711
## [292,] 4 1 0.3023802664
## [293,] 3 2 0.2483661545
## [294,] 1 4 0.4658581048
## [295,] 3 1 0.3799472096
## [296,] 3 5 0.2493078156
## [297,] 3 2 0.3722742721
## [298,] 3 5 0.3792318497
## [299,] 1 3 0.3307375935
## [300,] 1 4 0.2914342073
## [301,] 3 1 0.2585706637
## [302,] 2 1 0.1363411714
## [303,] 3 5 0.2493078156
## [304,] 3 1 0.2715641885
## [305,] 5 3 -0.0111422909
## [306,] 3 2 0.2543414060
## [307,] 3 1 0.2559524464
## [308,] 3 1 0.2568303357
## [309,] 5 3 0.1976626401
## [310,] 5 3 0.1898791585
## [311,] 3 1 0.2748705778
## [312,] 5 3 0.1733870724
## [313,] 1 4 0.2315716431
## [314,] 2 3 0.0670923444
## [315,] 3 5 0.3709647140
## [316,] 5 3 0.0123115762
## [317,] 2 5 -0.0034016294
## [318,] 3 5 0.4090472791
## [319,] 3 5 0.2514523691
## [320,] 3 1 0.3799472096
## [321,] 3 5 0.2262519943
## [322,] 2 3 0.1517700213
## [323,] 3 2 0.1962666184
## [324,] 1 4 0.1549601629
## [325,] 3 1 0.1567242967
## [326,] 3 2 0.3756658626
## [327,] 3 1 0.2279631505
## [328,] 4 1 0.1464565945
## [329,] 1 4 0.4612813739
## [330,] 3 1 0.2502203253
## [331,] 3 5 0.2466211115
## [332,] 5 3 0.0159666876
## [333,] 1 4 0.2425100578
## [334,] 3 2 0.1714419507
## [335,] 3 5 0.2190027125
## [336,] 2 3 0.2244443601
## [337,] 3 5 0.4090472791
## [338,] 1 4 0.4658581048
## [339,] 1 4 0.1549601629
## [340,] 2 3 0.1379529697
## [341,] 4 1 0.2766489842
## [342,] 1 4 0.4658581048
## [343,] 3 2 0.3168918287
## [344,] 2 3 0.2400425840
## [345,] 2 3 0.1745529276
## [346,] 3 1 0.2787752792
## [347,] 3 1 0.4049351429
## [348,] 3 5 0.2488546182
## [349,] 3 5 0.3709647140
## [350,] 3 5 0.2493078156
## [351,] 3 5 0.3709647140
## [352,] 1 4 0.2936815929
## [353,] 2 3 0.0786910496
## [354,] 3 1 0.1951074258
## [355,] 3 5 0.2488546182
## [356,] 1 4 0.2914342073
## [357,] 3 5 0.3296952105
## [358,] 4 1 0.1464565945
## [359,] 3 5 0.3666442121
## [360,] 5 4 0.1738087725
## [361,] 3 5 0.4090472791
## [362,] 3 1 0.4049351429
## [363,] 1 4 0.2914342073
## [364,] 3 5 0.2264775926
## [365,] 3 2 0.3756658626
## [366,] 5 4 0.1666545082
## [367,] 3 5 0.3267162327
## [368,] 1 4 0.0480582466
## [369,] 3 5 0.1801618098
## [370,] 3 1 0.1611764714
## [371,] 2 3 0.0309416246
## [372,] 3 1 0.1617585252
## [373,] 3 1 0.2748705778
## [374,] 5 3 -0.0123738269
## [375,] 3 1 0.2680125295
## [376,] 3 5 0.3709647140
## [377,] 3 5 0.2191718720
## [378,] 3 5 0.2172821175
## [379,] 1 4 0.4612813739
## [380,] 3 5 0.1817093053
## [381,] 3 2 0.3241997073
## [382,] 3 1 0.2748705778
## [383,] 3 5 0.3853469257
## [384,] 1 4 0.4612813739
## [385,] 3 2 0.1571676210
## [386,] 3 1 0.2787752792
## [387,] 5 4 0.2673319476
## [388,] 1 4 0.4658581048
## [389,] 1 3 0.3307375935
## [390,] 3 2 0.2638095754
## [391,] 3 5 0.2514523691
## [392,] 3 2 0.1962666184
## [393,] 3 1 0.3077997238
## [394,] 3 2 0.2670060455
## [395,] 3 5 0.2514523691
## [396,] 3 1 0.2526441555
## [397,] 3 1 0.1573408081
## [398,] 1 4 0.2626154838
## [399,] 3 5 0.3866198920
## [400,] 5 4 0.2084784276
## [401,] 3 5 0.2069547514
## [402,] 5 3 0.0123115762
## [403,] 3 5 0.4090472791
## [404,] 4 1 0.0888669700
## [405,] 3 5 0.2488546182
## [406,] 2 3 0.2768711161
## [407,] 3 5 0.2493078156
## [408,] 1 3 0.3307375935
## [409,] 3 5 0.2225980305
## [410,] 3 5 0.0828624774
## [411,] 3 5 0.1166830468
## [412,] 5 3 0.1043309256
## [413,] 1 4 0.0677851478
## [414,] 1 4 0.0677851478
## [415,] 3 5 0.0636198967
## [416,] 2 5 0.1586659576
## [417,] 3 5 0.0619379497
## [418,] 4 1 0.0917482183
## [419,] 3 5 0.0675598828
## [420,] 5 3 0.2048199010
## [421,] 5 3 0.3077633118
## [422,] 3 5 0.0595985061
## [423,] 4 1 0.0543354598
## [424,] 5 3 0.1583578804
## [425,] 3 5 0.0538582565
## [426,] 3 5 0.0702738244
## [427,] 5 3 0.1297295024
## [428,] 5 4 0.2557825628
## [429,] 5 4 0.1117585475
## [430,] 4 1 0.0240998032
## [431,] 1 4 0.0677851478
## [432,] 1 4 0.0484681761
## [433,] 3 5 0.1166830468
## [434,] 1 4 0.0677851478
## [435,] 1 4 0.0484681761
## [436,] 4 1 0.2598986545
## [437,] 5 3 0.1936868523
## [438,] 3 5 0.0773010074
## [439,] 1 4 0.0285171900
## [440,] 1 4 0.0484681761
## [441,] 1 4 0.0677851478
## [442,] 3 5 0.1114671190
## [443,] 4 1 0.2598986545
## [444,] 3 5 0.1166830468
## [445,] 4 1 0.2742283814
## [446,] 4 1 0.0543354598
## [447,] 5 4 0.0909716673
## [448,] 3 5 0.1114671190
## [449,] 5 3 0.1248256504
## [450,] 3 5 0.0702738244
## [451,] 4 1 0.2598986545
## [452,] 3 5 0.0550905234
## [453,] 2 5 0.1725853199
## [454,] 5 4 0.1803528569
## [455,] 4 5 0.3086725076
## [456,] 4 1 0.2563314929
## [457,] 5 4 0.3226486493
## [458,] 3 5 0.2751805222
## [459,] 2 4 0.1351820835
## [460,] 5 3 0.1043309256
## [461,] 5 3 0.1670184908
## [462,] 1 4 0.0484681761
## [463,] 3 1 0.2416539316
## [464,] 2 3 0.1875737556
## [465,] 4 1 0.3830358670
## [466,] 5 3 0.1381725112
## [467,] 3 5 0.0887462242
## [468,] 2 3 0.2191313660
## [469,] 3 5 0.0842243128
## [470,] 4 1 0.3086209684
## [471,] 3 5 0.0745351599
## [472,] 5 3 0.1882979999
## [473,] 2 3 0.0819398097
## [474,] 5 3 0.1786995616
## [475,] 5 3 0.0892775747
## [476,] 2 5 -0.0267120813
## [477,] 4 5 0.3783393351
## [478,] 3 5 0.0841269792
## [479,] 5 3 0.3503751160
## [480,] 2 3 -0.1088460735
## [481,] 3 5 0.1114671190
## [482,] 2 3 0.0326734230
## [483,] 5 3 0.1106187436
## [484,] 5 4 0.2454455420
## [485,] 5 4 0.2978287865
## [486,] 4 1 0.3506277676
## [487,] 4 1 0.2306049289
## [488,] 4 1 0.3086209684
## [489,] 5 3 0.1100220014
## [490,] 4 1 0.2803045374
## [491,] 5 3 0.1648494366
## [492,] 5 3 0.1883685259
## [493,] 5 3 0.1424268749
## [494,] 5 3 0.3600080561
## [495,] 4 1 0.2389778876
## [496,] 5 4 0.1954975049
## [497,] 5 4 0.1920800948
## [498,] 5 4 0.2426516984
## [499,] 4 1 0.3086209684
## [500,] 4 1 0.3877893476
## [501,] 5 3 0.3237378093
## [502,] 4 1 0.2272413569
## [503,] 5 3 0.2797188928
## [504,] 1 3 0.2048598220
## [505,] 4 1 0.2598986545
## [506,] 5 4 0.2283342494
## [507,] 5 4 0.1214010652
## [508,] 4 5 0.2490361390
## [509,] 5 3 0.1565670074
## [510,] 4 1 0.4223771441
## [511,] 5 3 0.2048199010
## [512,] 4 1 0.2306049289
## [513,] 4 1 0.3086209684
## [514,] 5 3 0.1675845530
## [515,] 5 3 0.1595345051
## [516,] 5 4 0.1920800948
## [517,] 5 3 0.1301380916
## [518,] 5 3 0.1936868523
## [519,] 4 1 0.1079868328
## [520,] 5 4 0.1376159147
## [521,] 4 1 0.2598986545
## [522,] 4 1 0.3286759607
## [523,] 5 4 0.2934013180
## [524,] 4 1 0.4008302200
## [525,] 5 4 0.2952442859
## [526,] 4 1 0.2803045374
## [527,] 4 5 0.3652526479
## [528,] 5 4 0.3747922491
## [529,] 5 4 0.3747922491
## [530,] 5 3 0.2523995492
## [531,] 5 4 0.2032262043
## [532,] 5 3 0.2520828223
## [533,] 4 1 0.4693352560
## [534,] 5 3 0.2917902881
## [535,] 5 3 0.3166827608
## [536,] 5 3 0.3166827608
## [537,] 4 1 0.3116087987
## [538,] 5 3 0.3166827608
## [539,] 4 5 0.3844615055
## [540,] 4 1 0.2802603839
## [541,] 4 1 0.3150795247
## [542,] 5 4 0.2587315303
## [543,] 2 5 0.1610670466
## [544,] 5 4 0.3282820965
## [545,] 4 1 0.3720550548
## [546,] 4 1 0.3756124994
## [547,] 4 1 0.3560682951
## [548,] 5 4 0.0746479468
## [549,] 5 4 0.1161076956
## [550,] 4 5 0.3014774679
## [551,] 5 4 0.2560662659
## [552,] 5 3 0.3166827608
## [553,] 5 4 0.3209002071
## [554,] 5 4 0.1474830023
## [555,] 5 4 0.3747922491
## [556,] 5 4 0.2248381900
## [557,] 4 5 0.3708447589
## [558,] 5 4 0.2101261584
## [559,] 5 4 0.1524963731
## [560,] 5 4 0.2587315303
## [561,] 5 4 0.2118669800
## [562,] 5 4 0.1703448963
## [563,] 4 5 0.3855320928
## [564,] 5 3 0.2164812666
## [565,] 5 4 0.3336078302
## [566,] 5 4 0.1923872489
## [567,] 5 4 0.2087740756
## [568,] 5 4 0.3747922491
## [569,] 5 4 0.2556905764
## [570,] 4 5 0.2510449414
## [571,] 4 1 0.3986255906
## [572,] 5 4 0.1644491934
## [573,] 5 4 0.3118516057
## [574,] 5 4 0.1813364011
## [575,] 4 1 0.3720550548
## [576,] 5 3 0.2658673222
## [577,] 5 3 0.2968530469
## [578,] 5 3 0.2917902881
## [579,] 4 5 0.4032579264
## [580,] 5 4 0.2837366019
## [581,] 2 5 0.0883497931
## [582,] 4 5 0.3765011638
## [583,] 5 4 0.1757606035
## [584,] 4 1 0.2945648169
## [585,] 5 3 0.3166827608
## [586,] 4 5 0.4545906307
## [587,] 5 3 0.3166827608
## [588,] 5 3 0.3166827608
## [589,] 5 3 0.2797203276
## [590,] 5 4 0.0865152793
## [591,] 5 4 0.2087740756
## [592,] 4 5 0.4789958293
## [593,] 5 4 0.3509919657
## [594,] 5 4 0.0974454876
## [595,] 5 4 0.3282820965
## [596,] 5 4 0.3747922491
## [597,] 5 3 0.2283141822
## [598,] 5 4 0.2467940803
## [599,] 2 5 0.1783304460
## [600,] 4 1 0.3756124994
## [601,] 4 1 0.3986255906
## [602,] 5 3 0.2986999041
## [603,] 2 5 0.0772627185
## [604,] 5 3 0.1001487624
## [605,] 5 4 0.3095627651
## [606,] 4 1 0.3486062266
## [607,] 5 4 0.2424109517
## [608,] 4 1 0.3720550548
## [609,] 4 5 0.3844615055
## [610,] 5 4 0.1560148907
## [611,] 5 3 0.1673400205
## [612,] 5 3 0.2884144286
## [613,] 5 4 0.1854705612
## [614,] 5 4 0.1915755076
## [615,] 4 1 0.3986255906
## [616,] 4 1 0.3756124994
## [617,] 5 3 0.2720273845
## [618,] 4 5 0.3855320928
## [619,] 5 4 0.2063944157
## [620,] 5 4 0.2587315303
## [621,] 5 4 0.3336078302
## [622,] 4 1 0.3756124994
## [623,] 5 4 0.0694934937
## [624,] 5 3 0.3166827608
## [625,] 5 4 0.2343105370
## [626,] 5 4 0.3350884058
## [627,] 2 5 0.1006221848
## [628,] 5 4 0.1143396536
## [629,] 5 3 0.2986999041
## [630,] 5 4 0.1839706819
## [631,] 5 4 0.0842531815
## [632,] 4 5 0.3708447589
## [633,] 4 5 0.3708447589
## [634,] 5 4 0.2117478091
## [635,] 5 4 0.3509919657
## [636,] 4 5 0.3708447589
## [637,] 4 5 0.1839317179
## [638,] 5 4 0.1240974834
## [639,] 4 5 0.2503689724
## [640,] 4 1 0.3598083052
## [641,] 5 2 0.0771914851
## [642,] 4 1 0.4693352560
## [643,] 4 5 0.4789958293
## [644,] 5 4 0.0815549014
## [645,] 5 4 0.3204066485
## [646,] 4 1 0.4523993604
## [647,] 5 4 0.3747922491
## [648,] 2 5 0.1162089988
## [649,] 4 5 0.3296149648
## [650,] 4 5 0.2548424881
## [651,] 4 5 0.4032277250
## [652,] 5 2 0.1191359531
## [653,] 5 4 0.2947228888
## [654,] 4 5 0.4789958293
## [655,] 5 4 0.3747922491
## [656,] 5 4 0.2780794995
## [657,] 5 4 0.2448770292
## [658,] 2 4 0.1399307881
## [659,] 5 4 0.1867084266
## [660,] 4 5 0.3310540479
## [661,] 5 4 0.2172025107
## [662,] 4 5 0.3185714440
## [663,] 5 4 0.3095627651
## [664,] 5 4 0.2173834312
## [665,] 5 4 0.1769943204
## [666,] 5 4 0.1560148907
## [667,] 5 4 0.3747922491
## [668,] 5 4 0.3204066485
## [669,] 4 5 0.2845050939
## [670,] 4 5 0.4032277250
## [671,] 5 4 0.3163198638
## [672,] 4 5 0.4157629587
## [673,] 5 4 0.1769943204
## [674,] 5 4 0.1762610655
## [675,] 4 5 0.3855320928
## [676,] 4 5 0.3862034302
## [677,] 2 5 0.1148530907
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = km$cluster, dist = dist(predictors))
## attr(,"class")
## [1] "silhouette"
#decrase the cluster by 1 cause on label encoding we start with 0
nc<-km$cluster-1
# bcubed precision and recall
bcubed(nc, df$depressiveness)
## precision recall
## 0.7076679 0.2806891
df1<-data.frame(Actual=df$depressiveness,cluster=nc)
head(df1,10)
## Actual cluster
## 1 1 2
## 2 1 2
## 3 1 2
## 4 0 1
## 5 1 2
## 6 1 0
## 7 1 2
## 8 1 0
## 9 0 2
## 10 1 2
#length
length(df1$Actual)
## [1] 677
length(df1$cluster)
## [1] 677
#count number of cluster with matched vlaues
cont_table <- table(df1$Actual, df1$cluster)
cont_table
##
## 0 1 2 3 4
## 0 1 64 77 1 51
## 1 101 10 173 87 112
differences <- sum(apply(cont_table, 1, max)) - sum(diag(cont_table))
print(paste("difference: ",differences))
## [1] "difference: 239"
#perform k-means clustering with k = 4 clusters
km <- kmeans(predictors, centers = 4, nstart = 25)
#view results
km
## K-means clustering with 4 clusters of sizes 188, 163, 251, 75
##
## Cluster means:
## school_year age gender bmi who_bmi phq_score
## 1 3.446809 22.13830 0.4361702 1.398936 3.244681 1.132979
## 2 2.159509 19.92638 0.6196319 1.269939 3.171779 0.000000
## 3 1.565737 19.07570 0.4621514 1.370518 3.107570 1.294821
## 4 1.840000 19.81333 0.3066667 1.493333 3.226667 2.533333
## depression_severity suicidal depression_diagnosis depression_treatment
## 1 0.6861702 0.9308511 0.8776596 0.9148936
## 2 3.0000000 0.9938650 0.9570552 0.9631902
## 3 0.2948207 0.9123506 0.9362550 0.9402390
## 4 1.6666667 0.6533333 0.8400000 0.8400000
## gad_score anxiety_severity anxiousness anxiety_diagnosis anxiety_treatment
## 1 0.9840426 0.6117021 0.81382979 0.8989362 0.9308511
## 2 0.2699387 1.4969325 0.98773006 0.9570552 0.9570552
## 3 1.0000000 0.8127490 0.72908367 0.9362550 0.9163347
## 4 2.7866667 2.6533333 0.02666667 0.8666667 0.8400000
## epworth_score sleepiness
## 1 0.15425532 0.8031915
## 2 0.04294479 0.9325153
## 3 0.16334661 0.8007968
## 4 0.62666667 0.5466667
##
## Clustering vector:
## [1] 3 3 3 4 3 2 3 2 3 3 2 3 3 3 3 3 3 3 2 3 2 3 4 2 4 2 2 3 3 2 2 2 3 4 3 3 3
## [38] 2 3 2 2 3 3 3 3 2 3 3 3 3 3 2 3 2 3 1 3 3 4 2 4 3 3 2 3 2 3 2 3 2 2 3 3 3
## [75] 3 3 4 2 3 3 2 2 3 3 3 4 3 3 3 3 3 3 2 3 3 2 2 3 3 2 4 3 3 3 2 3 3 4 3 3 2
## [112] 4 4 3 3 3 3 3 2 4 2 4 3 3 4 3 3 4 3 3 2 3 2 4 3 3 3 3 4 3 3 3 2 4 2 3 2 3
## [149] 3 3 3 3 4 3 2 2 2 3 3 2 3 4 4 2 3 1 3 4 3 3 4 3 4 2 1 3 3 2 4 3 3 3 2 2 2
## [186] 3 3 1 4 2 2 3 3 3 2 3 4 3 2 3 2 2 4 4 4 4 1 3 1 2 4 4 3 2 4 2 2 3 2 3 3 2
## [223] 3 4 2 2 2 3 3 3 2 3 3 4 3 2 3 2 1 3 3 3 4 3 2 3 1 3 1 3 2 3 3 3 1 4 3 4 3
## [260] 4 3 2 1 3 3 3 3 4 3 4 3 3 4 2 3 3 3 4 4 4 3 2 1 3 3 3 3 1 1 3 3 2 3 2 3 3
## [297] 3 3 2 2 3 4 3 3 1 3 3 3 1 1 3 1 2 4 3 1 4 3 3 3 3 4 3 2 3 3 3 2 2 3 3 1 2
## [334] 3 3 4 3 2 2 4 2 2 3 4 4 3 3 3 3 3 3 2 4 3 3 2 3 2 3 1 3 3 2 3 3 1 3 2 3 3
## [371] 4 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 1 2 2 3 3 3 3 3 3 3 3 2 3 1 3 1 3 2 3 4 3
## [408] 2 3 3 3 1 2 2 3 4 3 2 3 1 1 3 2 1 3 3 1 1 1 2 2 2 3 2 2 2 1 3 2 2 2 3 2 3
## [445] 2 2 1 3 1 3 2 3 4 1 1 2 1 3 4 1 1 2 3 4 2 1 3 4 3 2 3 1 4 1 1 4 1 3 1 4 3
## [482] 4 1 1 1 2 2 2 1 2 1 1 1 1 2 1 1 1 2 2 1 2 1 2 2 1 1 1 1 2 1 2 2 1 1 1 1 1
## [519] 2 1 2 2 1 2 1 2 1 1 1 1 1 1 2 1 1 1 2 1 2 2 2 1 4 1 2 2 2 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 4 1 1 2 1 2 1 1 1 1 1 2
## [593] 1 1 1 1 1 1 4 2 2 1 4 1 1 2 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 4 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 1 2 1 4 1 1 1 1 1 2 1 1 1 4 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 4
##
## Within cluster sum of squares by cluster:
## [1] 1403.5319 881.3742 1264.1195 698.1867
## (between_SS / total_SS = 43.7 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
#plot results of k-means
fviz_cluster(km, data =predictors )
clusplot(df, km$cluster, color = TRUE, shade = TRUE, labels = 4)
# Evaluate and compare the results
silhouette_score <- silhouette(km$cluster, dist(predictors))
silhouette_score
## cluster neighbor sil_width
## [1,] 3 4 0.2210384908
## [2,] 3 2 0.4087595983
## [3,] 3 2 0.4146277604
## [4,] 4 3 0.2369589392
## [5,] 3 4 0.3488714356
## [6,] 2 3 0.3051132267
## [7,] 3 2 0.3370931029
## [8,] 2 3 0.2082425793
## [9,] 3 2 0.3197459319
## [10,] 3 2 0.3341522301
## [11,] 2 3 0.3051132267
## [12,] 3 2 0.3160150888
## [13,] 3 2 0.4087595983
## [14,] 3 4 0.2766583685
## [15,] 3 2 0.3148643680
## [16,] 3 4 0.3764058542
## [17,] 3 2 0.4432199819
## [18,] 3 2 0.3181782403
## [19,] 2 3 0.3051132267
## [20,] 3 4 0.3002971074
## [21,] 2 3 0.3051132267
## [22,] 3 4 0.3843922982
## [23,] 4 3 0.1333513903
## [24,] 2 3 0.3631265862
## [25,] 4 3 0.0234768317
## [26,] 2 3 0.3051132267
## [27,] 2 3 0.3631265862
## [28,] 3 2 0.3370931029
## [29,] 3 2 0.3370931029
## [30,] 2 3 0.3756957496
## [31,] 2 3 0.3756957496
## [32,] 2 3 0.1056070099
## [33,] 3 2 0.4205223312
## [34,] 4 3 -0.0116896781
## [35,] 3 2 0.3751550987
## [36,] 3 2 0.4157159730
## [37,] 3 2 0.3353487232
## [38,] 2 3 0.3051132267
## [39,] 3 2 0.3448566662
## [40,] 2 3 0.2808920685
## [41,] 2 3 0.3051132267
## [42,] 3 4 0.3086148538
## [43,] 3 2 0.4186900160
## [44,] 3 2 0.4157159730
## [45,] 3 4 0.2095364304
## [46,] 2 3 0.1521961693
## [47,] 3 4 0.2203015443
## [48,] 3 2 0.3421148586
## [49,] 3 4 0.3566249540
## [50,] 3 2 0.3191965360
## [51,] 3 2 0.4514986981
## [52,] 2 3 0.0987463970
## [53,] 3 4 0.2766583685
## [54,] 2 3 0.0978254590
## [55,] 3 4 0.0854508295
## [56,] 1 3 0.2048047576
## [57,] 3 2 0.3577338600
## [58,] 3 2 0.3160150888
## [59,] 4 3 0.1934219952
## [60,] 2 3 0.3051132267
## [61,] 4 2 -0.0292332083
## [62,] 3 2 0.2316179183
## [63,] 3 4 0.3286244221
## [64,] 2 3 0.3837615843
## [65,] 3 4 0.3410541492
## [66,] 2 3 0.2000466951
## [67,] 3 2 0.4360099411
## [68,] 2 3 0.2412791030
## [69,] 3 4 0.1937490576
## [70,] 2 3 0.2414145353
## [71,] 2 3 0.2670185294
## [72,] 3 4 0.2690535594
## [73,] 3 2 0.3448566662
## [74,] 3 2 0.3246107255
## [75,] 3 4 0.2766583685
## [76,] 3 4 0.2400592376
## [77,] 4 3 0.1236092962
## [78,] 2 3 0.3756957496
## [79,] 3 2 0.4143635812
## [80,] 3 4 0.2866663812
## [81,] 2 3 0.1593700443
## [82,] 2 3 0.3051132267
## [83,] 3 2 0.3246107255
## [84,] 3 2 0.3556609037
## [85,] 3 2 0.4360099411
## [86,] 4 3 -0.0102966040
## [87,] 3 2 0.4360099411
## [88,] 3 2 0.4168140203
## [89,] 3 4 0.3488714356
## [90,] 3 2 0.4432199819
## [91,] 3 2 0.4135559672
## [92,] 3 2 0.3160150888
## [93,] 2 3 0.3051132267
## [94,] 3 2 0.3421148586
## [95,] 3 2 0.3178697032
## [96,] 2 3 0.3051132267
## [97,] 2 3 0.3051132267
## [98,] 3 4 0.2242897011
## [99,] 3 2 0.3322443591
## [100,] 2 3 0.3051132267
## [101,] 4 3 0.1236092962
## [102,] 3 4 0.2719528730
## [103,] 3 2 0.3448566662
## [104,] 3 2 0.4096950173
## [105,] 2 3 0.3051132267
## [106,] 3 2 0.4157159730
## [107,] 3 2 0.4360099411
## [108,] 4 3 -0.0024988312
## [109,] 3 2 0.4289599714
## [110,] 3 2 0.4289599714
## [111,] 2 3 0.3631265862
## [112,] 4 3 0.1772789987
## [113,] 4 3 0.1883580855
## [114,] 3 2 0.2370504099
## [115,] 3 2 0.2173530281
## [116,] 3 2 0.4096950173
## [117,] 3 2 0.1538076812
## [118,] 3 2 0.1251236801
## [119,] 2 3 0.3051132267
## [120,] 4 3 0.1700992748
## [121,] 2 3 0.3631265862
## [122,] 4 3 0.1236092962
## [123,] 3 2 0.3160150888
## [124,] 3 4 0.2115676111
## [125,] 4 3 0.1470799086
## [126,] 3 2 0.4096950173
## [127,] 3 4 0.3764058542
## [128,] 4 1 0.1046329575
## [129,] 3 4 0.2114101858
## [130,] 3 4 0.2719528730
## [131,] 2 3 0.2082425793
## [132,] 3 2 0.3448566662
## [133,] 2 3 0.3959911058
## [134,] 4 3 0.2422380639
## [135,] 3 2 0.3341522301
## [136,] 3 2 0.3222937255
## [137,] 3 4 0.2719528730
## [138,] 3 2 0.3448566662
## [139,] 4 3 0.2416601282
## [140,] 3 2 0.1985535887
## [141,] 3 2 0.3246107255
## [142,] 3 2 0.3002735074
## [143,] 2 3 0.2414145353
## [144,] 4 3 -0.0816274265
## [145,] 2 3 0.2000466951
## [146,] 3 2 0.3072379977
## [147,] 2 3 0.2000466951
## [148,] 3 2 0.4360099411
## [149,] 3 4 0.2182118228
## [150,] 3 2 0.3448566662
## [151,] 3 4 0.2191412314
## [152,] 3 2 0.3370931029
## [153,] 4 3 0.0975361528
## [154,] 3 4 0.1711871100
## [155,] 2 3 0.2808920685
## [156,] 2 3 0.3537674381
## [157,] 2 3 0.2534437344
## [158,] 3 2 0.4432199819
## [159,] 3 4 0.3384994725
## [160,] 2 3 0.2534437344
## [161,] 3 2 0.4357034195
## [162,] 4 1 0.0782222102
## [163,] 4 3 0.0321936106
## [164,] 2 3 0.1123953743
## [165,] 3 2 0.1810663944
## [166,] 1 3 0.2011513347
## [167,] 3 2 0.2723337128
## [168,] 4 3 0.1973441961
## [169,] 3 4 0.2858234707
## [170,] 3 2 0.3556609037
## [171,] 4 3 -0.0097766315
## [172,] 3 2 0.3421148586
## [173,] 4 3 0.0485282651
## [174,] 2 3 0.3631265862
## [175,] 1 3 0.1644198654
## [176,] 3 4 0.2346603294
## [177,] 3 4 0.1421870979
## [178,] 2 3 0.3631265862
## [179,] 4 3 0.3063290523
## [180,] 3 2 0.3205937304
## [181,] 3 2 0.4135559672
## [182,] 3 1 0.1085618081
## [183,] 2 3 0.1908944637
## [184,] 2 3 0.3756957496
## [185,] 2 3 0.3756957496
## [186,] 3 1 0.3090953782
## [187,] 3 1 0.3709975307
## [188,] 1 3 0.1838418148
## [189,] 4 2 0.0033491946
## [190,] 2 3 0.1943099322
## [191,] 2 3 0.3959911058
## [192,] 3 1 0.1139115073
## [193,] 3 1 0.3285045151
## [194,] 3 4 0.3817568832
## [195,] 2 3 0.3631265862
## [196,] 3 2 0.4165710443
## [197,] 4 1 0.1140629111
## [198,] 3 2 0.3994599055
## [199,] 2 3 0.3631265862
## [200,] 3 2 0.2850923887
## [201,] 2 3 0.3234134334
## [202,] 2 3 0.2578677731
## [203,] 4 3 0.2840206904
## [204,] 4 3 0.0024791598
## [205,] 4 3 0.1116746067
## [206,] 4 3 0.2873945042
## [207,] 1 3 0.1339765665
## [208,] 3 1 0.1430400741
## [209,] 1 3 0.0316812915
## [210,] 2 3 0.3434397729
## [211,] 4 2 0.2966087761
## [212,] 4 3 0.2239276534
## [213,] 3 2 0.3994599055
## [214,] 2 3 0.1661319395
## [215,] 4 2 0.2452515956
## [216,] 2 3 0.3756957496
## [217,] 2 3 0.3299009774
## [218,] 3 2 0.3991759923
## [219,] 2 3 0.1526908910
## [220,] 3 4 0.3462505765
## [221,] 3 2 0.2703312073
## [222,] 2 3 0.1697761948
## [223,] 3 2 0.4432199819
## [224,] 4 3 0.1849398784
## [225,] 2 3 0.3631265862
## [226,] 2 3 0.2414145353
## [227,] 2 3 0.3756957496
## [228,] 3 1 0.3480985467
## [229,] 3 2 0.3550340297
## [230,] 3 4 0.2690535594
## [231,] 2 3 0.2508363909
## [232,] 3 4 0.1941119721
## [233,] 3 2 0.3421148586
## [234,] 4 3 0.0597976346
## [235,] 3 1 0.3691991572
## [236,] 2 3 0.2665442594
## [237,] 3 2 0.2666922915
## [238,] 2 1 0.0637076285
## [239,] 1 3 0.0005639306
## [240,] 3 2 0.2532062416
## [241,] 3 1 0.3248531452
## [242,] 3 1 0.1612055644
## [243,] 4 3 0.2694164727
## [244,] 3 1 0.2957061997
## [245,] 2 1 0.2816346177
## [246,] 3 1 0.4391376709
## [247,] 1 3 -0.0547117586
## [248,] 3 1 0.2497298189
## [249,] 1 3 0.2965974936
## [250,] 3 1 0.4173362572
## [251,] 2 3 0.4006433462
## [252,] 3 2 0.1567773785
## [253,] 3 2 0.2948731213
## [254,] 3 2 0.2665825477
## [255,] 1 3 0.1451224263
## [256,] 4 3 0.2737155571
## [257,] 3 4 0.1701576144
## [258,] 4 3 0.2848455033
## [259,] 3 1 0.2406550817
## [260,] 4 3 0.3213561301
## [261,] 3 2 0.2484893976
## [262,] 2 3 0.2396966396
## [263,] 1 2 0.0092179555
## [264,] 3 1 0.2629988888
## [265,] 3 4 0.1691808055
## [266,] 3 1 0.4352026034
## [267,] 3 1 0.1780370301
## [268,] 4 3 0.2646247665
## [269,] 3 2 0.3197301590
## [270,] 4 3 0.3029783486
## [271,] 3 2 0.3632930401
## [272,] 3 4 0.2541694768
## [273,] 4 3 0.0091229609
## [274,] 2 3 0.1514630508
## [275,] 3 2 0.2355908550
## [276,] 3 4 0.2287993101
## [277,] 3 4 0.3778212707
## [278,] 4 3 0.0036064311
## [279,] 4 3 0.1846081085
## [280,] 4 3 0.2164847065
## [281,] 3 2 0.2044678809
## [282,] 2 3 0.3929095679
## [283,] 1 4 0.2769256202
## [284,] 3 1 0.4152390557
## [285,] 3 2 0.3229155251
## [286,] 3 4 0.3408840819
## [287,] 3 1 0.2957061997
## [288,] 1 3 -0.0420817875
## [289,] 1 3 0.1175810007
## [290,] 3 1 0.4391376709
## [291,] 3 2 0.3327535043
## [292,] 2 1 0.1338418398
## [293,] 3 4 0.2541694768
## [294,] 2 3 0.4108293856
## [295,] 3 2 0.4303443670
## [296,] 3 1 0.2945473624
## [297,] 3 4 0.3778212707
## [298,] 3 1 0.4092573756
## [299,] 2 3 0.2396966396
## [300,] 2 3 0.4501229338
## [301,] 3 2 0.2874817691
## [302,] 4 2 0.1491052038
## [303,] 3 1 0.2945473624
## [304,] 3 2 0.3113359871
## [305,] 1 3 -0.0367467945
## [306,] 3 4 0.2594552454
## [307,] 3 2 0.2865595159
## [308,] 3 2 0.3004833565
## [309,] 1 3 0.1668465317
## [310,] 1 3 0.1358391134
## [311,] 3 2 0.3197301590
## [312,] 1 3 0.1361252717
## [313,] 2 3 0.3784515597
## [314,] 4 3 0.0533716117
## [315,] 3 1 0.4001093494
## [316,] 1 3 -0.0547117586
## [317,] 4 1 0.0195726295
## [318,] 3 1 0.4391376709
## [319,] 3 2 0.2665825477
## [320,] 3 2 0.4303443670
## [321,] 3 1 0.2618188985
## [322,] 4 3 0.1521455558
## [323,] 3 4 0.2033831847
## [324,] 2 3 0.2968106899
## [325,] 3 2 0.1903942437
## [326,] 3 4 0.3806680481
## [327,] 3 2 0.2540701949
## [328,] 2 1 0.2538490004
## [329,] 2 3 0.3968577339
## [330,] 3 2 0.3197414375
## [331,] 3 2 0.2716873143
## [332,] 1 3 -0.0534847228
## [333,] 2 3 0.1870792589
## [334,] 3 4 0.1769485428
## [335,] 3 1 0.2621129610
## [336,] 4 3 0.2122369790
## [337,] 3 1 0.4391376709
## [338,] 2 3 0.4108293856
## [339,] 2 3 0.2968106899
## [340,] 4 3 0.1268743000
## [341,] 2 1 0.2685756935
## [342,] 2 3 0.4108293856
## [343,] 3 4 0.3229678351
## [344,] 4 3 0.2289835299
## [345,] 4 3 0.1734217262
## [346,] 3 2 0.3251254866
## [347,] 3 1 0.4352026034
## [348,] 3 1 0.2957061997
## [349,] 3 1 0.4001093494
## [350,] 3 1 0.2945473624
## [351,] 3 1 0.4001093494
## [352,] 2 3 0.4360862262
## [353,] 4 3 0.0645826143
## [354,] 3 2 0.2321800353
## [355,] 3 1 0.2957061997
## [356,] 2 3 0.4501229338
## [357,] 3 1 0.3591246298
## [358,] 2 1 0.2538490004
## [359,] 3 1 0.3975940329
## [360,] 1 3 0.2161982868
## [361,] 3 1 0.4391376709
## [362,] 3 1 0.4352026034
## [363,] 2 3 0.4501229338
## [364,] 3 2 0.2484893976
## [365,] 3 4 0.3806680481
## [366,] 1 3 0.2012773367
## [367,] 3 1 0.3552747787
## [368,] 2 3 0.2239633438
## [369,] 3 1 0.2198445154
## [370,] 3 2 0.1992433193
## [371,] 4 3 0.0271293346
## [372,] 3 2 0.2080468794
## [373,] 3 2 0.3197301590
## [374,] 3 1 0.0717198235
## [375,] 3 2 0.2979620126
## [376,] 3 1 0.4001093494
## [377,] 3 1 0.2629988888
## [378,] 3 1 0.2626960454
## [379,] 2 3 0.3968577339
## [380,] 3 1 0.2223377766
## [381,] 3 4 0.3297049159
## [382,] 3 2 0.3197301590
## [383,] 3 1 0.4152390557
## [384,] 2 3 0.3968577339
## [385,] 3 4 0.1628490764
## [386,] 3 2 0.3251254866
## [387,] 1 3 0.2373506132
## [388,] 2 3 0.4108293856
## [389,] 2 3 0.2396966396
## [390,] 3 4 0.2690872875
## [391,] 3 2 0.2665825477
## [392,] 3 4 0.2033831847
## [393,] 3 2 0.3368742814
## [394,] 3 4 0.2728441797
## [395,] 3 2 0.2665825477
## [396,] 3 2 0.2948731213
## [397,] 3 2 0.1567773785
## [398,] 2 3 0.4006433462
## [399,] 3 1 0.4173362572
## [400,] 1 3 0.2965974936
## [401,] 3 1 0.2497298189
## [402,] 1 3 -0.0547117586
## [403,] 3 1 0.4391376709
## [404,] 2 1 0.2816346177
## [405,] 3 1 0.2957061997
## [406,] 4 3 0.2694164727
## [407,] 3 1 0.2945473624
## [408,] 2 3 0.2396966396
## [409,] 3 2 0.2539335960
## [410,] 3 1 0.1295661997
## [411,] 3 1 0.1612212312
## [412,] 1 3 0.0539293140
## [413,] 2 3 0.4360055022
## [414,] 2 3 0.4360055022
## [415,] 3 1 0.1114671034
## [416,] 4 1 0.1629672752
## [417,] 3 1 0.1016129096
## [418,] 2 3 0.2718666230
## [419,] 3 1 0.1314591229
## [420,] 1 3 0.1369838054
## [421,] 1 3 0.2576497126
## [422,] 3 1 0.0859963726
## [423,] 2 3 0.4104091322
## [424,] 1 3 0.1140560714
## [425,] 3 1 0.1123850179
## [426,] 3 1 0.1156324019
## [427,] 1 3 0.0886787013
## [428,] 1 3 0.2538185153
## [429,] 1 2 0.2838465530
## [430,] 2 3 0.3125884652
## [431,] 2 3 0.4360055022
## [432,] 2 3 0.4477035829
## [433,] 3 1 0.1612212312
## [434,] 2 3 0.4360055022
## [435,] 2 3 0.4477035829
## [436,] 2 1 0.3901102634
## [437,] 1 3 0.1286539422
## [438,] 3 1 0.1308511105
## [439,] 2 3 0.2771387412
## [440,] 2 3 0.4477035829
## [441,] 2 3 0.4360055022
## [442,] 3 1 0.1578843159
## [443,] 2 1 0.3901102634
## [444,] 3 1 0.1612212312
## [445,] 2 1 0.2156666715
## [446,] 2 3 0.4104091322
## [447,] 1 2 0.2624974047
## [448,] 3 1 0.1578843159
## [449,] 1 3 0.0786999740
## [450,] 3 1 0.1156324019
## [451,] 2 1 0.3901102634
## [452,] 3 1 0.0870758619
## [453,] 4 1 0.1894556519
## [454,] 1 3 0.2229653116
## [455,] 1 2 0.0686880486
## [456,] 2 1 0.1999635960
## [457,] 1 3 0.3727643530
## [458,] 3 1 0.3130303880
## [459,] 4 1 0.1868544065
## [460,] 1 3 0.0539293140
## [461,] 1 3 0.1095986014
## [462,] 2 3 0.4477035829
## [463,] 3 2 0.2454843255
## [464,] 4 3 0.1771782016
## [465,] 2 1 0.2321205143
## [466,] 1 3 0.0808227324
## [467,] 3 1 0.1486757421
## [468,] 4 3 0.2073236624
## [469,] 3 2 0.1122904577
## [470,] 2 1 0.3560680218
## [471,] 3 1 0.1353435697
## [472,] 1 3 0.1236282734
## [473,] 4 3 0.0685032867
## [474,] 1 3 0.1165052747
## [475,] 1 3 0.0489749586
## [476,] 4 1 -0.0087016881
## [477,] 1 2 -0.0621887925
## [478,] 3 1 0.1424024918
## [479,] 1 3 0.3028216019
## [480,] 4 3 -0.1150773741
## [481,] 3 1 0.1578843159
## [482,] 4 3 0.0280136704
## [483,] 1 3 0.0584621055
## [484,] 1 3 0.3415737726
## [485,] 1 3 0.3616960882
## [486,] 2 1 0.2107426380
## [487,] 2 1 0.3727459590
## [488,] 2 1 0.3560680218
## [489,] 1 3 0.0590823014
## [490,] 2 1 0.3394693738
## [491,] 1 3 0.1056300165
## [492,] 1 3 0.1285860553
## [493,] 1 3 0.0785552219
## [494,] 1 3 0.3102650528
## [495,] 2 1 0.1593429016
## [496,] 1 3 0.2266462734
## [497,] 1 3 0.3108047809
## [498,] 1 3 0.2387436044
## [499,] 2 1 0.3560680218
## [500,] 2 1 0.2032912753
## [501,] 1 3 0.2792942024
## [502,] 2 1 0.3560276472
## [503,] 1 3 0.2326183125
## [504,] 2 3 0.1558176889
## [505,] 2 1 0.3901102634
## [506,] 1 3 0.2346157631
## [507,] 1 2 0.2199310583
## [508,] 1 2 0.0943515563
## [509,] 1 3 0.1083406998
## [510,] 2 1 0.2243826060
## [511,] 1 3 0.1369838054
## [512,] 2 1 0.3727459590
## [513,] 2 1 0.3560680218
## [514,] 1 3 0.1080848406
## [515,] 1 3 0.1059689289
## [516,] 1 3 0.3108047809
## [517,] 1 3 0.0951075541
## [518,] 1 3 0.1286539422
## [519,] 2 3 0.2824881714
## [520,] 1 2 0.3095697502
## [521,] 2 1 0.3901102634
## [522,] 2 1 0.1968336648
## [523,] 1 3 0.2578776830
## [524,] 2 1 0.2470788241
## [525,] 1 3 0.3502487631
## [526,] 2 1 0.3394693738
## [527,] 1 2 -0.0197430845
## [528,] 1 3 0.3867588016
## [529,] 1 3 0.3867588016
## [530,] 1 3 0.1992377012
## [531,] 1 4 0.3574872571
## [532,] 1 3 0.2081520216
## [533,] 2 1 0.1649589268
## [534,] 1 3 0.2392197792
## [535,] 1 3 0.2613396322
## [536,] 1 3 0.2613396322
## [537,] 2 1 0.2487359433
## [538,] 1 3 0.2613396322
## [539,] 2 1 0.1048703202
## [540,] 2 1 0.2022919337
## [541,] 2 1 0.0929914108
## [542,] 1 3 0.2454735793
## [543,] 4 1 0.1622456911
## [544,] 1 3 0.3283464134
## [545,] 2 1 0.2930560685
## [546,] 2 1 0.1244355070
## [547,] 2 1 0.1059170261
## [548,] 1 4 0.0723861139
## [549,] 1 2 0.2435133686
## [550,] 1 2 0.0335590915
## [551,] 1 3 0.3590987649
## [552,] 1 3 0.2613396322
## [553,] 1 3 0.3364369775
## [554,] 1 2 0.3146715486
## [555,] 1 3 0.3867588016
## [556,] 1 3 0.2159269390
## [557,] 1 2 0.0249186053
## [558,] 1 3 0.1753704766
## [559,] 1 2 0.3427187074
## [560,] 1 3 0.2454735793
## [561,] 1 3 0.2039042377
## [562,] 1 3 0.1423706509
## [563,] 1 2 0.0183526229
## [564,] 1 3 0.1813505997
## [565,] 1 3 0.3637681383
## [566,] 1 3 0.1706247076
## [567,] 1 3 0.2293799627
## [568,] 1 3 0.3867588016
## [569,] 1 3 0.2526169549
## [570,] 1 2 0.1239184693
## [571,] 2 1 0.2686029812
## [572,] 1 2 0.3483750797
## [573,] 1 3 0.3213336235
## [574,] 1 3 0.2715606842
## [575,] 2 1 0.2930560685
## [576,] 1 3 0.2204796961
## [577,] 1 3 0.2445212751
## [578,] 1 3 0.2392197792
## [579,] 2 1 0.1141076172
## [580,] 1 3 0.2985951218
## [581,] 4 1 0.0913410792
## [582,] 1 2 0.0140136806
## [583,] 1 2 0.3464867242
## [584,] 2 1 0.2331754125
## [585,] 1 3 0.2613396322
## [586,] 2 1 0.1327486453
## [587,] 1 3 0.2613396322
## [588,] 1 3 0.2613396322
## [589,] 1 3 0.2332215497
## [590,] 1 4 0.0966740680
## [591,] 1 3 0.2293799627
## [592,] 2 1 0.1488791795
## [593,] 1 3 0.3742061955
## [594,] 1 4 0.0908492688
## [595,] 1 3 0.3283464134
## [596,] 1 3 0.3867588016
## [597,] 1 3 0.1782394482
## [598,] 1 3 0.3092992330
## [599,] 4 3 0.1791229344
## [600,] 2 1 0.1244355070
## [601,] 2 1 0.2686029812
## [602,] 1 3 0.2466267430
## [603,] 4 1 0.0937958536
## [604,] 1 3 0.0388620340
## [605,] 1 3 0.4114526170
## [606,] 2 1 0.2764985076
## [607,] 1 3 0.3291276736
## [608,] 2 1 0.2930560685
## [609,] 2 1 0.1048703202
## [610,] 1 3 0.1666096937
## [611,] 1 3 0.1342001946
## [612,] 1 3 0.2398395874
## [613,] 1 3 0.1772372521
## [614,] 1 3 0.1891190602
## [615,] 2 1 0.2686029812
## [616,] 2 1 0.1244355070
## [617,] 1 3 0.2236650373
## [618,] 1 2 0.0183526229
## [619,] 1 3 0.2022796765
## [620,] 1 3 0.2454735793
## [621,] 1 3 0.3637681383
## [622,] 2 1 0.1244355070
## [623,] 1 2 0.1466516325
## [624,] 1 3 0.2613396322
## [625,] 1 3 0.2138636940
## [626,] 1 3 0.4245404082
## [627,] 4 1 0.1041472018
## [628,] 1 3 0.1835278889
## [629,] 1 3 0.2466267430
## [630,] 1 2 0.3908120551
## [631,] 1 2 0.2461147491
## [632,] 1 2 0.0249186053
## [633,] 1 2 0.0249186053
## [634,] 1 3 0.2984279629
## [635,] 1 3 0.3742061955
## [636,] 1 2 0.0249186053
## [637,] 1 2 0.1488402902
## [638,] 1 3 0.1603913356
## [639,] 1 2 0.0251935595
## [640,] 2 1 0.2442618582
## [641,] 1 4 0.0645023262
## [642,] 2 1 0.1649589268
## [643,] 2 1 0.1488791795
## [644,] 1 2 0.2400174243
## [645,] 1 3 0.4192909658
## [646,] 2 1 0.1490812090
## [647,] 1 3 0.3867588016
## [648,] 4 1 0.1194899633
## [649,] 1 2 0.1183090730
## [650,] 1 2 0.1736602507
## [651,] 1 2 -0.0207636130
## [652,] 1 4 0.1003598682
## [653,] 1 3 0.4064630992
## [654,] 2 1 0.1488791795
## [655,] 1 3 0.3867588016
## [656,] 1 3 0.4210273585
## [657,] 1 3 0.3518436637
## [658,] 4 1 0.1677077216
## [659,] 1 3 0.1658048245
## [660,] 1 2 0.1161170739
## [661,] 1 3 0.3467686799
## [662,] 1 2 0.0812434352
## [663,] 1 3 0.4114526170
## [664,] 1 3 0.3201748289
## [665,] 1 3 0.2899393557
## [666,] 1 3 0.1666096937
## [667,] 1 3 0.3867588016
## [668,] 1 3 0.4192909658
## [669,] 1 2 0.0170973994
## [670,] 1 2 -0.0207636130
## [671,] 1 3 0.3569346947
## [672,] 1 2 -0.0246545901
## [673,] 1 3 0.2899393557
## [674,] 1 2 0.3523650934
## [675,] 1 2 0.0183526229
## [676,] 1 2 0.0071693994
## [677,] 4 1 0.1185685511
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = km$cluster, dist = dist(predictors))
## attr(,"class")
## [1] "silhouette"
#decrase the cluster by 1 cause on label encoding we start with 0
nc<-km$cluster-1
# bcubed precision and recall
bcubed(nc, df$depressiveness)
## precision recall
## 0.7039766 0.3276772
df1<-data.frame(Actual=df$depressiveness,cluster=nc)
head(df1,10)
## Actual cluster
## 1 1 2
## 2 1 2
## 3 1 2
## 4 0 3
## 5 1 2
## 6 1 1
## 7 1 2
## 8 1 1
## 9 0 2
## 10 1 2
#length
length(df1$Actual)
## [1] 677
length(df1$cluster)
## [1] 677
#count number of cluster with matched vlaues
cont_table <- table(df1$Actual, df1$cluster)
cont_table
##
## 0 1 2 3
## 0 51 1 77 65
## 1 137 162 174 10
differences <- sum(apply(cont_table, 1, max)) - sum(diag(cont_table))
print(paste("difference: ",differences))
## [1] "difference: 38"
#perform k-means clustering with k = 3 clusters
km <- kmeans(predictors, centers = 3, nstart = 25)
#view results
km
## K-means clustering with 3 clusters of sizes 310, 203, 164
##
## Cluster means:
## school_year age gender bmi who_bmi phq_score
## 1 1.564516 19.10968 0.4290323 1.390323 3.125806 1.51290323
## 2 3.413793 22.12808 0.4285714 1.408867 3.246305 1.25615764
## 3 2.152439 19.92683 0.6219512 1.274390 3.176829 0.02439024
## depression_severity suicidal depression_diagnosis depression_treatment
## 1 0.5322581 0.8645161 0.9258065 0.9290323
## 2 0.7832512 0.9113300 0.8669951 0.9014778
## 3 3.0060976 0.9878049 0.9512195 0.9573171
## gad_score anxiety_severity anxiousness anxiety_diagnosis anxiety_treatment
## 1 1.3419355 1.1548387 0.5935484 0.9258065 0.9096774
## 2 1.1280788 0.7783251 0.7536946 0.8965517 0.9162562
## 3 0.2682927 1.5000000 0.9878049 0.9512195 0.9512195
## epworth_score sleepiness
## 1 0.23225806 0.7612903
## 2 0.21674877 0.7733990
## 3 0.04878049 0.9268293
##
## Clustering vector:
## [1] 1 1 1 1 1 3 1 3 1 1 3 1 1 1 1 1 1 1 3 1 3 1 1 3 1 3 3 1 1 3 3 3 1 1 1 1 1
## [38] 3 1 3 3 1 1 1 1 3 1 1 1 1 1 3 1 3 1 2 1 1 1 3 1 1 1 3 1 3 1 3 1 3 3 1 1 1
## [75] 1 1 1 3 1 1 3 3 1 1 1 1 1 1 1 1 1 1 3 1 1 3 3 1 1 3 1 1 1 1 3 1 1 1 1 1 3
## [112] 1 1 1 1 1 1 1 3 1 3 1 1 1 1 1 1 2 1 1 3 1 3 1 1 1 1 1 1 1 1 1 3 1 3 1 3 1
## [149] 1 1 1 1 1 1 3 3 3 1 1 3 1 2 1 3 1 2 1 1 1 1 1 1 1 3 2 1 1 3 1 1 1 1 3 3 3
## [186] 1 1 2 3 3 3 1 1 1 3 1 2 1 3 1 3 3 1 1 1 1 2 1 2 3 1 1 1 3 1 3 3 1 3 1 1 3
## [223] 1 1 3 3 3 1 1 1 3 1 1 1 1 3 1 3 2 1 1 1 1 1 3 1 2 1 2 1 3 1 1 1 2 1 1 1 1
## [260] 1 1 3 2 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 3 2 1 1 1 1 2 2 1 1 3 1 3 1 1
## [297] 1 1 3 3 1 1 1 1 2 1 1 1 2 2 1 2 3 1 1 2 2 1 1 1 1 1 1 3 1 1 1 3 3 1 1 2 3
## [334] 1 1 1 1 3 3 1 3 3 1 1 1 1 1 1 1 1 1 3 1 1 1 3 1 3 1 2 1 1 3 1 1 2 1 3 1 1
## [371] 1 1 1 1 1 1 1 1 3 1 1 1 1 3 1 1 2 3 3 1 1 1 1 1 1 1 1 3 1 2 1 2 1 3 1 1 1
## [408] 3 1 1 1 2 3 3 1 2 1 3 1 2 2 1 3 2 1 1 2 2 2 3 3 3 1 3 3 3 2 1 3 3 3 1 3 1
## [445] 3 3 2 1 2 1 3 1 2 2 2 3 2 1 2 2 2 3 1 1 3 2 1 1 1 3 1 2 1 2 2 2 2 1 2 1 1
## [482] 1 2 2 2 3 3 3 2 3 2 2 2 2 3 2 2 2 3 3 2 3 2 3 3 2 2 2 2 3 2 3 3 2 2 2 2 2
## [519] 3 2 3 3 2 3 2 3 2 2 2 2 2 2 3 2 2 2 3 2 3 3 3 2 2 2 3 3 3 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 3 2 2 2 3 2 2 2 2 3 2 3 2 2 2 2 2 3
## [593] 2 2 2 2 2 2 1 3 3 2 2 2 2 3 2 3 3 2 2 2 2 2 3 3 2 2 2 2 2 3 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 3 2 3 3 2 2 3 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2
##
## Within cluster sum of squares by cluster:
## [1] 2170.4387 1747.1823 907.7012
## (between_SS / total_SS = 36.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
#plot results of k-means
fviz_cluster(km, data = predictors)
clusplot(df, km$cluster, color = TRUE, shade = TRUE, labels = 3)
# Evaluate and compare the results
silhouette_score <- silhouette(km$cluster, dist(predictors))
silhouette_score
## cluster neighbor sil_width
## [1,] 1 3 0.221390124
## [2,] 1 3 0.328803751
## [3,] 1 3 0.324023749
## [4,] 1 3 0.215057944
## [5,] 1 3 0.352433358
## [6,] 3 1 0.330303818
## [7,] 1 3 0.260334701
## [8,] 3 1 0.252694193
## [9,] 1 3 0.244781549
## [10,] 1 3 0.250989127
## [11,] 3 1 0.330303818
## [12,] 1 3 0.239930958
## [13,] 1 3 0.328803751
## [14,] 1 3 0.335179375
## [15,] 1 3 0.249969099
## [16,] 1 3 0.377477828
## [17,] 1 3 0.352727899
## [18,] 1 3 0.248050648
## [19,] 3 1 0.330303818
## [20,] 1 3 0.347532090
## [21,] 3 1 0.330303818
## [22,] 1 3 0.372988784
## [23,] 1 3 0.245138527
## [24,] 3 1 0.385208741
## [25,] 1 3 0.171779918
## [26,] 3 1 0.330303818
## [27,] 3 1 0.385208741
## [28,] 1 3 0.260334701
## [29,] 1 3 0.260334701
## [30,] 3 1 0.398164046
## [31,] 3 1 0.398164046
## [32,] 3 1 0.148987595
## [33,] 1 3 0.331697038
## [34,] 1 3 0.286973542
## [35,] 1 3 0.303962673
## [36,] 1 3 0.336968739
## [37,] 1 3 0.268162248
## [38,] 3 1 0.330303818
## [39,] 1 3 0.269775176
## [40,] 3 1 0.306208303
## [41,] 3 1 0.330303818
## [42,] 1 3 0.347410376
## [43,] 1 3 0.335435312
## [44,] 1 3 0.336968739
## [45,] 1 3 0.309984715
## [46,] 3 1 0.189643598
## [47,] 1 3 0.321987090
## [48,] 1 3 0.260979093
## [49,] 1 3 0.362100116
## [50,] 1 3 0.243814303
## [51,] 1 3 0.353871928
## [52,] 3 1 0.140830674
## [53,] 1 3 0.335179375
## [54,] 3 1 0.136420176
## [55,] 1 3 0.162326122
## [56,] 2 3 0.204742918
## [57,] 1 3 0.287751409
## [58,] 1 3 0.239930958
## [59,] 1 3 0.239126990
## [60,] 3 1 0.330303818
## [61,] 1 3 -0.009967044
## [62,] 1 3 0.194650345
## [63,] 1 3 0.365991844
## [64,] 3 1 0.403185767
## [65,] 1 2 0.317563183
## [66,] 3 1 0.244056270
## [67,] 1 3 0.344206372
## [68,] 3 1 0.266278964
## [69,] 1 2 0.195811389
## [70,] 3 1 0.279810452
## [71,] 3 1 0.292762937
## [72,] 1 3 0.335103286
## [73,] 1 3 0.269775176
## [74,] 1 3 0.256295717
## [75,] 1 3 0.335179375
## [76,] 1 3 0.321416027
## [77,] 1 3 0.268073925
## [78,] 3 1 0.398164046
## [79,] 1 3 0.331806041
## [80,] 1 2 0.300864849
## [81,] 3 1 0.178449134
## [82,] 3 1 0.330303818
## [83,] 1 3 0.256295717
## [84,] 1 3 0.290907023
## [85,] 1 3 0.344206372
## [86,] 1 3 0.217089274
## [87,] 1 3 0.344206372
## [88,] 1 3 0.332084984
## [89,] 1 3 0.352433358
## [90,] 1 3 0.352727899
## [91,] 1 3 0.327813225
## [92,] 1 3 0.239930958
## [93,] 3 1 0.330303818
## [94,] 1 3 0.260979093
## [95,] 1 3 0.256654141
## [96,] 3 1 0.330303818
## [97,] 3 1 0.330303818
## [98,] 1 3 0.233891619
## [99,] 1 3 0.271342421
## [100,] 3 1 0.330303818
## [101,] 1 3 0.268073925
## [102,] 1 3 0.343703460
## [103,] 1 3 0.269775176
## [104,] 1 3 0.325412290
## [105,] 3 1 0.330303818
## [106,] 1 3 0.336968739
## [107,] 1 3 0.344206372
## [108,] 1 3 0.292957123
## [109,] 1 3 0.344009765
## [110,] 1 3 0.344009765
## [111,] 3 1 0.385208741
## [112,] 1 3 0.260331509
## [113,] 1 3 0.189267960
## [114,] 1 3 0.190009838
## [115,] 1 3 0.176521176
## [116,] 1 3 0.325412290
## [117,] 1 3 0.109916371
## [118,] 1 3 0.079666840
## [119,] 3 1 0.330303818
## [120,] 1 3 0.261937894
## [121,] 3 1 0.385208741
## [122,] 1 3 0.268073925
## [123,] 1 3 0.239930958
## [124,] 1 3 0.325169631
## [125,] 1 3 0.251378640
## [126,] 1 3 0.325412290
## [127,] 1 3 0.377477828
## [128,] 2 1 0.076917950
## [129,] 1 3 0.323103164
## [130,] 1 3 0.343703460
## [131,] 3 1 0.252694193
## [132,] 1 3 0.269775176
## [133,] 3 1 0.415828930
## [134,] 1 3 0.073332223
## [135,] 1 3 0.250989127
## [136,] 1 3 0.260568597
## [137,] 1 3 0.343703460
## [138,] 1 3 0.269775176
## [139,] 1 3 0.149308578
## [140,] 1 3 0.156605592
## [141,] 1 3 0.256295717
## [142,] 1 3 0.240564797
## [143,] 3 1 0.279810452
## [144,] 1 3 0.234217452
## [145,] 3 1 0.244056270
## [146,] 1 3 0.256024172
## [147,] 3 1 0.244056270
## [148,] 1 3 0.344206372
## [149,] 1 3 0.226977055
## [150,] 1 3 0.269775176
## [151,] 1 3 0.291329503
## [152,] 1 3 0.260334701
## [153,] 1 3 0.267692847
## [154,] 1 3 0.266114836
## [155,] 3 1 0.306208303
## [156,] 3 1 0.373776555
## [157,] 3 1 0.279002613
## [158,] 1 3 0.352727899
## [159,] 1 3 0.337690974
## [160,] 3 1 0.279002613
## [161,] 1 3 0.344358192
## [162,] 2 1 0.079527162
## [163,] 1 2 0.119047556
## [164,] 3 1 0.141640132
## [165,] 1 3 0.145582913
## [166,] 2 3 0.203645125
## [167,] 1 3 0.202111069
## [168,] 1 3 0.260074314
## [169,] 1 2 0.282341739
## [170,] 1 3 0.290907023
## [171,] 1 3 0.207238776
## [172,] 1 3 0.260979093
## [173,] 1 3 0.281061503
## [174,] 3 1 0.385208741
## [175,] 2 1 0.180014230
## [176,] 1 3 0.316734358
## [177,] 1 3 0.201731931
## [178,] 3 1 0.385208741
## [179,] 1 3 0.176966356
## [180,] 1 3 0.263090920
## [181,] 1 3 0.327813225
## [182,] 1 2 0.088410792
## [183,] 3 1 0.208644844
## [184,] 3 1 0.398164046
## [185,] 3 1 0.398164046
## [186,] 1 2 0.246516820
## [187,] 1 2 0.294470346
## [188,] 2 3 0.184344266
## [189,] 3 1 0.051778390
## [190,] 3 1 0.230073961
## [191,] 3 1 0.415828930
## [192,] 1 2 0.098691117
## [193,] 1 2 0.262158640
## [194,] 1 3 0.381977366
## [195,] 3 1 0.385208741
## [196,] 1 3 0.332002637
## [197,] 2 3 0.053525281
## [198,] 1 3 0.315586569
## [199,] 3 1 0.385208741
## [200,] 1 3 0.239883443
## [201,] 3 1 0.346279034
## [202,] 3 1 0.290409191
## [203,] 1 3 0.060867972
## [204,] 1 3 0.294293189
## [205,] 1 3 0.148184933
## [206,] 1 3 0.207191304
## [207,] 2 1 0.135532750
## [208,] 1 2 0.103270227
## [209,] 2 1 0.054455518
## [210,] 3 1 0.362846732
## [211,] 1 3 0.046348538
## [212,] 1 3 0.231756446
## [213,] 1 3 0.315586569
## [214,] 3 1 0.185796260
## [215,] 1 3 0.048801037
## [216,] 3 1 0.398164046
## [217,] 3 1 0.345566917
## [218,] 1 3 0.313540389
## [219,] 3 1 0.164545293
## [220,] 1 3 0.361669293
## [221,] 1 3 0.198409111
## [222,] 3 1 0.203423793
## [223,] 1 3 0.352727899
## [224,] 1 2 0.140647092
## [225,] 3 1 0.385208741
## [226,] 3 1 0.279810452
## [227,] 3 1 0.398164046
## [228,] 1 2 0.276216149
## [229,] 1 3 0.283239149
## [230,] 1 3 0.335103286
## [231,] 3 1 0.271896747
## [232,] 1 3 0.296201267
## [233,] 1 3 0.260979093
## [234,] 1 2 0.114112278
## [235,] 1 2 0.292030073
## [236,] 3 1 0.289572795
## [237,] 1 3 0.194042946
## [238,] 3 2 0.074995525
## [239,] 2 1 0.031405454
## [240,] 1 3 0.199349294
## [241,] 1 2 0.258811348
## [242,] 1 2 0.104364960
## [243,] 1 3 0.183702481
## [244,] 1 2 0.213658454
## [245,] 3 2 0.298115411
## [246,] 1 3 0.343433453
## [247,] 2 1 0.008759033
## [248,] 1 2 0.181303993
## [249,] 2 1 0.294337781
## [250,] 1 3 0.331965148
## [251,] 3 1 0.417966010
## [252,] 1 3 0.102707109
## [253,] 1 3 0.222773867
## [254,] 1 3 0.183036710
## [255,] 2 1 0.150640083
## [256,] 1 3 0.138913284
## [257,] 1 2 0.223918026
## [258,] 1 3 0.182520203
## [259,] 1 2 0.174115364
## [260,] 1 3 0.163238888
## [261,] 1 3 0.172524954
## [262,] 3 1 0.281285120
## [263,] 2 3 -0.012223786
## [264,] 1 2 0.229552388
## [265,] 1 3 0.307027687
## [266,] 1 3 0.332552130
## [267,] 1 2 0.139558370
## [268,] 1 3 0.184945090
## [269,] 1 3 0.233273717
## [270,] 1 3 0.194084016
## [271,] 1 3 0.283593167
## [272,] 1 3 0.329468660
## [273,] 1 3 0.224980804
## [274,] 3 1 0.194936685
## [275,] 1 3 0.196625514
## [276,] 1 3 0.322708054
## [277,] 1 3 0.370556356
## [278,] 1 3 0.274918809
## [279,] 1 3 0.257225399
## [280,] 1 2 0.210768313
## [281,] 1 3 0.174796194
## [282,] 3 1 0.410429685
## [283,] 2 1 0.284907547
## [284,] 1 3 0.322698769
## [285,] 1 3 0.260293476
## [286,] 1 2 0.348664095
## [287,] 1 2 0.213658454
## [288,] 2 1 0.003021519
## [289,] 2 3 0.115478127
## [290,] 1 3 0.343433453
## [291,] 1 3 0.264373697
## [292,] 3 2 0.146418868
## [293,] 1 3 0.329468660
## [294,] 3 1 0.431666240
## [295,] 1 3 0.338154217
## [296,] 1 2 0.211898861
## [297,] 1 3 0.370556356
## [298,] 1 3 0.320332153
## [299,] 3 1 0.281285120
## [300,] 3 1 0.467627123
## [301,] 1 3 0.214927573
## [302,] 1 3 0.013284752
## [303,] 1 2 0.211898861
## [304,] 1 3 0.228948466
## [305,] 2 1 -0.012663295
## [306,] 1 3 0.318847574
## [307,] 1 3 0.217333764
## [308,] 1 3 0.230769862
## [309,] 2 1 0.180959974
## [310,] 2 1 0.146109264
## [311,] 1 3 0.233273717
## [312,] 2 1 0.138675131
## [313,] 3 1 0.395419289
## [314,] 1 3 0.278544821
## [315,] 1 3 0.311009224
## [316,] 2 1 0.008759033
## [317,] 2 1 0.054539260
## [318,] 1 3 0.343433453
## [319,] 1 3 0.183036710
## [320,] 1 3 0.338154217
## [321,] 1 2 0.202443986
## [322,] 1 3 0.131817296
## [323,] 1 2 0.246001637
## [324,] 3 1 0.332970379
## [325,] 1 3 0.150152852
## [326,] 1 3 0.361118961
## [327,] 1 3 0.188403594
## [328,] 3 2 0.270635485
## [329,] 3 1 0.417408021
## [330,] 1 3 0.241562434
## [331,] 1 3 0.191088794
## [332,] 2 1 0.008925852
## [333,] 3 1 0.206075023
## [334,] 1 3 0.298163729
## [335,] 1 2 0.188854549
## [336,] 1 3 0.255516566
## [337,] 1 3 0.343433453
## [338,] 3 1 0.431666240
## [339,] 3 1 0.332970379
## [340,] 1 3 0.262818474
## [341,] 3 2 0.279133870
## [342,] 3 1 0.431666240
## [343,] 1 2 0.351036291
## [344,] 1 2 0.187791193
## [345,] 1 3 0.125615890
## [346,] 1 3 0.241399505
## [347,] 1 3 0.332552130
## [348,] 1 2 0.213658454
## [349,] 1 3 0.311009224
## [350,] 1 2 0.211898861
## [351,] 1 3 0.311009224
## [352,] 3 1 0.453317056
## [353,] 1 2 0.200995177
## [354,] 1 3 0.179371627
## [355,] 1 2 0.213658454
## [356,] 3 1 0.467627123
## [357,] 1 3 0.279262136
## [358,] 3 2 0.270635485
## [359,] 1 3 0.322488782
## [360,] 2 1 0.224418287
## [361,] 1 3 0.343433453
## [362,] 1 3 0.332552130
## [363,] 3 1 0.467627123
## [364,] 1 3 0.172524954
## [365,] 1 3 0.361118961
## [366,] 2 1 0.207500143
## [367,] 1 3 0.277259808
## [368,] 3 2 0.236756373
## [369,] 1 2 0.164010627
## [370,] 1 3 0.148759144
## [371,] 1 3 0.169622483
## [372,] 1 3 0.164778778
## [373,] 1 3 0.233273717
## [374,] 1 2 0.043264852
## [375,] 1 3 0.219510150
## [376,] 1 3 0.311009224
## [377,] 1 2 0.229552388
## [378,] 1 2 0.230886800
## [379,] 3 1 0.417408021
## [380,] 1 2 0.193793999
## [381,] 1 3 0.333522028
## [382,] 1 3 0.233273717
## [383,] 1 3 0.322698769
## [384,] 3 1 0.417408021
## [385,] 1 2 0.232676372
## [386,] 1 3 0.241399505
## [387,] 2 1 0.239173265
## [388,] 3 1 0.431666240
## [389,] 3 1 0.281285120
## [390,] 1 2 0.293977126
## [391,] 1 3 0.183036710
## [392,] 1 2 0.246001637
## [393,] 1 3 0.265310140
## [394,] 1 2 0.313210348
## [395,] 1 3 0.183036710
## [396,] 1 3 0.222773867
## [397,] 1 3 0.102707109
## [398,] 3 1 0.417966010
## [399,] 1 3 0.331965148
## [400,] 2 1 0.294337781
## [401,] 1 2 0.181303993
## [402,] 2 1 0.008759033
## [403,] 1 3 0.343433453
## [404,] 3 2 0.298115411
## [405,] 1 2 0.213658454
## [406,] 1 3 0.183702481
## [407,] 1 2 0.211898861
## [408,] 3 1 0.281285120
## [409,] 1 3 0.180681383
## [410,] 1 2 0.117269371
## [411,] 1 2 0.107656547
## [412,] 2 1 0.087889200
## [413,] 3 1 0.451498912
## [414,] 3 1 0.451498912
## [415,] 1 2 0.062600659
## [416,] 2 1 0.105662517
## [417,] 1 2 0.058466889
## [418,] 3 2 0.294903939
## [419,] 1 2 0.062266719
## [420,] 2 1 0.168483411
## [421,] 2 1 0.258211498
## [422,] 1 2 0.078501551
## [423,] 3 1 0.426493242
## [424,] 2 1 0.135151471
## [425,] 1 2 0.051122596
## [426,] 1 2 0.065266039
## [427,] 2 1 0.116642140
## [428,] 2 1 0.250268770
## [429,] 2 3 0.269744256
## [430,] 3 1 0.328487681
## [431,] 3 1 0.451498912
## [432,] 3 1 0.463555239
## [433,] 1 2 0.107656547
## [434,] 3 1 0.451498912
## [435,] 3 1 0.463555239
## [436,] 3 2 0.398917895
## [437,] 2 1 0.162242541
## [438,] 1 2 0.101869416
## [439,] 3 1 0.290302517
## [440,] 3 1 0.463555239
## [441,] 3 1 0.451498912
## [442,] 1 2 0.105875468
## [443,] 3 2 0.398917895
## [444,] 1 2 0.107656547
## [445,] 3 2 0.237180728
## [446,] 3 1 0.426493242
## [447,] 2 3 0.254364591
## [448,] 1 2 0.105875468
## [449,] 2 1 0.080986129
## [450,] 1 2 0.065266039
## [451,] 3 2 0.398917895
## [452,] 1 2 0.052062619
## [453,] 2 1 0.002530591
## [454,] 2 1 0.232862653
## [455,] 2 3 0.039498483
## [456,] 3 2 0.221895160
## [457,] 2 1 0.369242100
## [458,] 1 2 0.239075904
## [459,] 2 3 0.031034594
## [460,] 2 1 0.087889200
## [461,] 2 1 0.139789125
## [462,] 3 1 0.463555239
## [463,] 1 3 0.176425545
## [464,] 1 2 0.118524263
## [465,] 3 2 0.245615023
## [466,] 2 1 0.095743978
## [467,] 1 2 0.116526951
## [468,] 1 2 0.133653857
## [469,] 1 3 0.065352657
## [470,] 3 2 0.365465166
## [471,] 1 2 0.064995845
## [472,] 2 1 0.155171605
## [473,] 1 2 0.120811351
## [474,] 2 1 0.149925799
## [475,] 2 1 0.075831060
## [476,] 2 1 0.055938968
## [477,] 2 3 -0.089848717
## [478,] 1 2 0.112965035
## [479,] 2 1 0.308569572
## [480,] 1 2 0.095664774
## [481,] 1 2 0.105875468
## [482,] 1 2 0.080503218
## [483,] 2 1 0.090752206
## [484,] 2 1 0.339445725
## [485,] 2 1 0.358715492
## [486,] 3 2 0.223540466
## [487,] 3 2 0.381751557
## [488,] 3 2 0.365465166
## [489,] 2 1 0.090066921
## [490,] 3 2 0.348982756
## [491,] 2 1 0.138536963
## [492,] 2 1 0.157970105
## [493,] 2 1 0.096015596
## [494,] 2 1 0.314552702
## [495,] 3 2 0.166492670
## [496,] 2 1 0.235542690
## [497,] 2 3 0.294265240
## [498,] 2 1 0.234873174
## [499,] 3 2 0.365465166
## [500,] 3 2 0.216091466
## [501,] 2 1 0.286500770
## [502,] 3 2 0.365442125
## [503,] 2 1 0.234712023
## [504,] 3 1 0.194288652
## [505,] 3 2 0.398917895
## [506,] 2 1 0.231666633
## [507,] 2 3 0.198758117
## [508,] 2 3 0.080499482
## [509,] 2 1 0.135611654
## [510,] 3 2 0.237858802
## [511,] 2 1 0.168483411
## [512,] 3 2 0.381751557
## [513,] 3 2 0.365465166
## [514,] 2 1 0.138531641
## [515,] 2 1 0.135741487
## [516,] 2 3 0.294265240
## [517,] 2 1 0.115117942
## [518,] 2 1 0.162242541
## [519,] 3 2 0.306318821
## [520,] 2 3 0.294408682
## [521,] 3 2 0.398917895
## [522,] 3 2 0.209865818
## [523,] 2 1 0.264802610
## [524,] 3 2 0.260442570
## [525,] 2 1 0.347938904
## [526,] 3 2 0.348982756
## [527,] 2 3 -0.037492700
## [528,] 2 1 0.380743382
## [529,] 2 1 0.380743382
## [530,] 2 1 0.202893677
## [531,] 2 3 0.368179816
## [532,] 2 1 0.221808191
## [533,] 3 2 0.181348752
## [534,] 2 1 0.250765333
## [535,] 2 1 0.270649731
## [536,] 2 1 0.270649731
## [537,] 3 2 0.260533262
## [538,] 2 1 0.270649731
## [539,] 3 2 0.130398058
## [540,] 3 2 0.213673444
## [541,] 3 2 0.115322126
## [542,] 2 1 0.251681076
## [543,] 2 1 0.055205649
## [544,] 2 1 0.323146249
## [545,] 3 2 0.304862800
## [546,] 3 2 0.150260860
## [547,] 3 2 0.132344171
## [548,] 2 1 0.129526961
## [549,] 2 3 0.228654176
## [550,] 2 3 0.006921305
## [551,] 2 1 0.354966216
## [552,] 2 1 0.270649731
## [553,] 2 1 0.336635712
## [554,] 2 3 0.298241917
## [555,] 2 1 0.380743382
## [556,] 2 1 0.223757939
## [557,] 2 3 -0.008227957
## [558,] 2 1 0.174916203
## [559,] 2 3 0.320962396
## [560,] 2 1 0.251681076
## [561,] 2 1 0.212508734
## [562,] 2 1 0.157565174
## [563,] 2 3 -0.013412037
## [564,] 2 1 0.194977769
## [565,] 2 1 0.359779344
## [566,] 2 1 0.185895749
## [567,] 2 1 0.236448029
## [568,] 2 1 0.380743382
## [569,] 2 1 0.252781222
## [570,] 2 3 0.109126991
## [571,] 3 2 0.280660105
## [572,] 2 3 0.326890791
## [573,] 2 1 0.317196020
## [574,] 2 1 0.267527999
## [575,] 3 2 0.304862800
## [576,] 2 1 0.233359705
## [577,] 2 1 0.255506885
## [578,] 2 1 0.250765333
## [579,] 3 2 0.129341316
## [580,] 2 1 0.294336661
## [581,] 2 1 0.158851003
## [582,] 2 3 -0.018246328
## [583,] 2 3 0.329998821
## [584,] 3 2 0.244946475
## [585,] 2 1 0.270649731
## [586,] 3 2 0.149076546
## [587,] 2 1 0.270649731
## [588,] 2 1 0.270649731
## [589,] 2 1 0.245243054
## [590,] 2 1 0.141725719
## [591,] 2 1 0.236448029
## [592,] 3 2 0.165106996
## [593,] 2 1 0.370107096
## [594,] 2 1 0.139145050
## [595,] 2 1 0.323146249
## [596,] 2 1 0.380743382
## [597,] 2 1 0.182787863
## [598,] 2 1 0.303686830
## [599,] 1 2 0.039549659
## [600,] 3 2 0.150260860
## [601,] 3 2 0.280660105
## [602,] 2 1 0.258583792
## [603,] 2 1 0.085234904
## [604,] 2 1 0.079836692
## [605,] 2 1 0.403017917
## [606,] 3 2 0.288505404
## [607,] 2 1 0.328049833
## [608,] 3 2 0.304862800
## [609,] 3 2 0.130398058
## [610,] 2 1 0.181393372
## [611,] 2 1 0.149901733
## [612,] 2 1 0.250411010
## [613,] 2 1 0.185836352
## [614,] 2 1 0.187136111
## [615,] 3 2 0.280660105
## [616,] 3 2 0.150260860
## [617,] 2 1 0.236775699
## [618,] 2 3 -0.013412037
## [619,] 2 1 0.210106247
## [620,] 2 1 0.251681076
## [621,] 2 1 0.359779344
## [622,] 3 2 0.150260860
## [623,] 2 3 0.128353263
## [624,] 2 1 0.270649731
## [625,] 2 1 0.221295653
## [626,] 2 1 0.415181066
## [627,] 2 1 0.138682631
## [628,] 2 1 0.187154575
## [629,] 2 1 0.258583792
## [630,] 2 3 0.368477585
## [631,] 2 3 0.227454344
## [632,] 2 3 -0.008227957
## [633,] 2 3 -0.008227957
## [634,] 2 3 0.287654394
## [635,] 2 1 0.370107096
## [636,] 2 3 -0.008227957
## [637,] 2 3 0.136803019
## [638,] 2 3 0.166782460
## [639,] 2 3 0.015060453
## [640,] 3 2 0.256092562
## [641,] 2 1 0.123435063
## [642,] 3 2 0.181348752
## [643,] 3 2 0.165106996
## [644,] 2 3 0.230941146
## [645,] 2 1 0.410714650
## [646,] 3 2 0.165670265
## [647,] 2 1 0.380743382
## [648,] 2 1 0.118199878
## [649,] 2 3 0.090177480
## [650,] 2 3 0.149227221
## [651,] 2 3 -0.040428577
## [652,] 2 1 0.182843918
## [653,] 2 1 0.398778132
## [654,] 3 2 0.165106996
## [655,] 2 1 0.380743382
## [656,] 2 3 0.404858411
## [657,] 2 1 0.347277807
## [658,] 2 3 0.068258526
## [659,] 2 1 0.168399559
## [660,] 2 3 0.086651267
## [661,] 2 1 0.342581571
## [662,] 2 3 0.064338641
## [663,] 2 1 0.403017917
## [664,] 2 1 0.318356735
## [665,] 2 3 0.274759123
## [666,] 2 1 0.181393372
## [667,] 2 1 0.380743382
## [668,] 2 1 0.410714650
## [669,] 2 3 -0.008528497
## [670,] 2 3 -0.040428577
## [671,] 2 1 0.354106355
## [672,] 2 3 -0.043685256
## [673,] 2 3 0.274759123
## [674,] 2 3 0.330088380
## [675,] 2 3 -0.013412037
## [676,] 2 3 -0.025731775
## [677,] 2 1 0.143071098
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = km$cluster, dist = dist(predictors))
## attr(,"class")
## [1] "silhouette"
#decrase the cluster by 1 cause on label encoding we start with 0
nc<-km$cluster-1
# bcubed precision and recall
bcubed(nc, df$depressiveness)
## precision recall
## 0.6421454 0.3959195
df1<-data.frame(Actual=df$depressiveness,cluster=nc)
head(df1,10)
## Actual cluster
## 1 1 0
## 2 1 0
## 3 1 0
## 4 0 0
## 5 1 0
## 6 1 2
## 7 1 0
## 8 1 2
## 9 0 0
## 10 1 0
#length
length(df1$Actual)
## [1] 677
length(df1$cluster)
## [1] 677
#count number of cluster with matched vlaues
cont_table <- table(df1$Actual, df1$cluster)
cont_table
##
## 0 1 2
## 0 127 65 2
## 1 183 138 162
differences <- sum(apply(cont_table, 1, max)) - sum(diag(cont_table))
print(paste("difference: ",differences))
## [1] "difference: 45"